Unlocking the potential of generative AI (GenAI) tools starts with empowering the people who use them. For developers, proper training is the bridge between understanding the capabilities of these tools and leveraging them to build innovative solutions. Effective training equips developers to harness these tools confidently, driving productivity and creating impactful applications.

The challenge lies in identifying training programs that address the unique needs of development teams. What skills should be prioritized? How do you ensure developers are equipped to solve real-world problems with generative AI?

In our recent AI Skills Report, we surveyed over 6,300 developers and tech leaders, and found a clear divide in how companies are preparing their engineering teams for the GenAI era. This blog dives into practical insights and strategies to help tech leaders build training programs that empower their teams to thrive.

The Good: Leading by Example

Structuring Training Programs. 

Some companies foster hands-on workshops and offer curated online courses tailored to developers. These organizations integrate GenAI training into everyday tasks such as coding, debugging, and project management, ensuring developers learn by doing. By encouraging practical usage, developers gain confidence in leveraging GenAI tools effectively.

Tip: Invest in Champions – Identify a group of early adopters who can act as “AI champions.” These champions can lead knowledge-sharing sessions, answer questions, and inspire broader adoption. This peer-led model creates an organic learning culture that resonates deeply with employees. In the words of one respondent:

“We created a group of developers who were eager to try out GenAI, and selected four as ‘champions.’ As everybody was invited to adopt it, these champions hosted sessions to answer questions and share knowledge and tips.”

Encouraging Innovation

Organizations ahead of the curve also experiment with AI-enabled tools, such as building proprietary AI chatbots for internal use. This not only enhances learning but also drives innovation.

Tip: Create a sandbox environment where developers can safely experiment with GenAI tools without fear of errors impacting production.

The Bad: Room for Improvement

Overrelying on Independent Learning

Some companies rely on self-education, offering minimal support to developers. While this approach might save costs, it risks leaving developers underprepared to use GenAI tools effectively. Plus, it misses out on the significant upside of upskilling, which has been found to boost employee retention.

Gaps in Edge-Case Training

Another common shortfall is focusing only on generic use cases without addressing GenAI’s limitations or potential ethical issues. For instance, developers may struggle to identify biases in AI outputs if these challenges are not explicitly discussed.

Tip: Focus on Edge Cases – Include examples of where GenAI tools might fail or behave unexpectedly. For example, discuss limitations like inaccurate contextual understanding or over-reliance on AI-generated solutions.

Building a Generative AI Training Framework

To effectively prepare developers for the GenAI era, organizations need a well-structured training framework that goes beyond surface-level knowledge, focusing on practical skills, ethical considerations, and real-world applications. 

Here’s how you can get started:

1. Strengthen Governance Frameworks

Beyond defining clear AI use policies, organizations should:

• Create tiered data access systems to ensure sensitive data is handled only by authorized personnel.
• Develop incident response protocols to address AI-related misuse or compliance breaches swiftly.
• Introduce AI Ethics Committees to oversee and review AI implementation, reducing risks of unintentional harm.

Resources like the OECD AI Policy Observatory offer guidelines for building ethical and compliant governance frameworks.

2. Enhance Training with Gamification and Role-Based Learning

Move beyond traditional training by:

• Incorporating gamification to engage developers in real-world AI problem-solving scenarios.
• Offering role-based modules: back-end teams can focus on coding and debugging with GenAI tools,, while data scientists could delve into AI model optimization.

Leverage resources like Coursera or edX for scalable training options.

3. Certification Programs with Real-World Impact

Certifications should validate skills while addressing business needs. To ensure relevance:

• Include hands-on projects as part of certification to simulate real-world tasks.
• Collaborate with well-known certification bodies, such as AI Standards Hub, for industry-recognized credentials.

4. Promote Collaboration 

To unlock AI’s potential, encourage:

• Cross-functional hackathons, where teams work on challenges like AI ethics or debugging.
• The establishment of knowledge-sharing platforms, such as Slack channels or internal forums, to share successful use cases.
• Collaboration with academic institutions and AI providers can greatly enrich training programs. 

Consider initiatives like the MIT AI Leadership Program, which provides executive education in AI, or partnerships with vendors such as OpenAI or Google Cloud, which often offer workshops tailored to their tools. By leveraging external expertise, companies can create a well-rounded training program without overburdening internal resources.

5. Leverage Metrics and AI-Driven Insights

Adopt AI tools to analyze training program effectiveness, such as tracking usage patterns of generative AI tools and correlating them with productivity metrics. This data-driven feedback loop helps refine training programs.

To ensure AI training programs are delivering value, companies should establish clear performance metrics. These might include:

• Adoption Rates: Track how often employees use generative AI tools post-training.
• Efficiency Gains: Measure reductions in time spent on routine tasks through AI.
• Employee Feedback: Conduct surveys to assess confidence levels in using AI tools.
• Internal Assessments: Test employees skills on HackerRank SkillUp to measure their progress and identify additional areas for growth.

By embedding these strategies, organizations can overcome common pitfalls, foster innovation, and maintain a competitive edge in the ever-evolving AI landscape.

Conclusion

Training developers on GenAI is necessary for companies aiming to remain competitive in 2024 and beyond. The best training programs combine structured learning, practical application, upskilling tools, and continuous improvement. 

By investing in their people, companies empower their developers and secure their position at the forefront of the AI revolution. To avoid being left behind, it’s time to rethink and refine your GenAI training strategy. For more insights, check out HackerRank’s report.

The post Training Developers on GenAI: Best Practices and Common Missteps appeared first on HackerRank Blog.

AI has evolved from an emerging trend to a critical driver of business performance, with companies hiring for AI skills consistently outperforming their peers. 

In our 2024 AI Skills Report,  we found that companies actively hiring for AI-related roles outperformed the market by 13% in share price growth between Q2 2023 and Q2 2024. AI is already having a noticeable impact, driving not just the bottom line of early adopters, but their value as well.

In this article, we delve into the latest findings from our AI adoption survey, exploring how companies leveraging AI talent are achieving exceptional growth and the steps tech teams can take to harness AI’s transformative potential.

Why Companies Can’t Afford to Delay AI Adoption

Despite its transformative potential, some companies remain hesitant to integrate AI, viewing it as a compliance or security risk rather than a value driver. However, delaying AI adoption can mean falling behind faster-moving competitors. 

HackerRank data shows that companies engaging in AI hiring posted a remarkable 40% growth in share price year-over-year, with the most engaged firms—those with 15 active tests that assess AI skills—achieving a 46% increase.

Companies that integrate AI effectively see tangible benefits beyond share price growth. For instance, AI-driven tools can reduce operational bottlenecks by automating repetitive tasks, freeing up teams to focus on strategic goals. 

Take e-commerce, for example. Companies in this industry that use AI for personalized recommendations report up to a 25% increase in sales conversions. For hiring managers, this highlights the importance of finding candidates who can deploy AI solutions that directly align with business priorities.

These numbers underscore the importance of acting now. But scaling hiring for cutting-edge disciplines like AI is easier said than done. Here are a few challenges you might face during your journey to embrace AI, and how to overcome them.

Overcoming Challenges in AI Hiring

1. The AI Skills Gap

One of the biggest barriers to hiring for AI is the skills gap. Many organizations struggle to find candidates who can manage complex AI implementations and align these technologies with business goals.

Solution

Future-proofing your AI hiring strategy doesn’t have to be daunting—it’s about staying informed and proactive. Roles like AI ethicists are essential for ensuring technologies align with ethical standards, while prompt engineers help fine-tune generative AI(GenAI)  tools for better performance. Here’s how to get ahead:

1. Spot Trends Early: Follow industry insights from platforms like LinkedIn and tech blogs. Emerging titles like AI compliance officers signal shifts in regulatory needs.
2. Upskill Internally: Offer training to current employees on AI tools or compliance frameworks.
3. Diversify Recruitment: Look for cross-disciplinary talent—ethics scholars or linguists might bring fresh perspectives.
4. Collaborate with Academia: Partner with universities to attract fresh graduates trained in cutting-edge AI.

By embracing these strategies, you’ll build a team equipped to navigate the evolving AI landscape​.

2. Unmonitored Generative AI Use

Developers and employees increasingly use generative AI tools, which, if unsupervised, could expose companies to compliance and data security risks. We recently found that 49% of developers use GenAI without company support.

Solution

To address the unmonitored use of generative AI effectively, businesses must combine policies, training, and oversight:

1. Establish AI Governance: Create comprehensive policies outlining acceptable uses, including restrictions on sensitive data input, ethical considerations, and compliance mandates.
2. Train Employees: Educate staff on the strengths, limitations, and risks of generative AI, emphasizing data privacy and security.
3. Implement Monitoring Tools: Use software to track AI interactions, ensuring policy adherence.
4. Foster a Culture of Accountability: Encourage employees to report potential misuse and provide regular updates on AI-related risks.
5. Stay Adaptive: Regularly review and revise policies as AI technology evolves.

How Companies Can Gain a Competitive Edge

Assess AI Skills Effectively

Relying on traditional resumes or interviews might not be enough when evaluating AI candidates. Instead, use technical skill assessments tailored to AI competencies, such as data modeling, natural language processing, or machine learning frameworks.

Invest in Ethical AI Practices

The rapid growth of AI adoption has brought ethical concerns to the forefront. Companies must ensure their AI initiatives are transparent and unbiased to avoid regulatory issues and reputational damage.

Organizations can incorporate fairness and bias audits into their AI workflows, ensuring that algorithms don’t inadvertently harm specific groups. Establishing a diverse AI ethics board can provide oversight and accountability.

Conclusion: Embracing AI to Stay Competitive

Companies that treat AI as an opportunity instead of a threat are already seeing substantial benefits, from market outperformance to operational efficiencies. By investing in AI hiring today, organizations can position themselves as leaders in their industries, ready to navigate the challenges and seize the opportunities of tomorrow.

The post Companies Hiring for AI Skills Outperform Market by 13% appeared first on HackerRank Blog.

Generative AI (GenAI) has become a transformative force in the tech industry. But as companies struggle to establish clear guidelines, developers are forging ahead—sometimes at odds with workplace policies. 

HackerRank’s 2024 AI Skills Report reveals an eye-opening trend: nearly half of developers are turning to AI tools independently, and nearly one in five are doing so despite outright bans from their organizations. This shows both the enormous value these tools bring and the significant challenges companies face in managing their adoption.

GenAI in the Workplace: The Data Speaks

Developers are flocking to tools like ChatGPT, GitHub, Copilot, and Tabnine because they offer real solutions. These tools save time by automating repetitive coding tasks, suggesting improvements, and helping developers explore new approaches to their work. However, many organizations aren’t providing adequate support for AI use. 

Our survey data found that workplace AI adoption is split into several approaches:

• 40% of developers use company-provided AI tools.
• 31% leverage their own AI tools with company approval, but without reimbursement.
• 18% of developers use AI despite its prohibition.
• 6% are reimbursed for AI tools they source themselves.
• 4% refrain entirely because of company prohibitions.

This highlights a core issue: developers see these tools as essential to their productivity, and restrictive policies push them to operate outside established guidelines. 

Developers want to work smarter, not harder. Generative AI tools allow them to code faster, debug with greater accuracy, and focus on creative problem-solving instead of repetitive tasks. It’s no surprise they’re pushing boundaries when they feel their companies aren’t providing the right tools or support.

These gaps between policy and practice can create risks for both sides.

When Restrictive AI Policies Backfire

Given that nearly half of all developers are using AI without employer support, it’s clear that restrictive policies can’t deter adoption of these. Instead, restrictive policies are driving unmonitored usage:

1. Shadow AI Practices: Developers might use personal accounts or external tools, circumventing IT departments entirely.
2. Compliance Concerns: Unapproved tools could lead to unintentional data exposure, especially when sensitive code or customer data is involved.
3. Missed Opportunities: By treating AI as a threat to mitigate, companies risk falling behind competitors that embrace it strategically.

Given these stakes, it’s clear that “not doing AI” is no longer an option. Companies that fail to align with their employees’ AI adoption risk alienating talent while exposing themselves to greater risks.

The Path Forward for Tech Employers

Organizations need to embrace GenAI responsibly if they want to keep their teams productive and engaged. The first step is to listen. Developers should be involved in shaping AI policies since they use these tools daily. Clear guidelines about approved uses—like debugging code or automating documentation—can go a long way in avoiding confusion. With those guidelines in place, there are a number of strategies employers can implement to guide how developers implement AI.

Embrace AI Upskilling

Companies should prioritize training and upskilling their tech teams on GenAI. In addition to improving AI skills, companies should train their developers on the ethical and security implications of AI. Providing workshops and hands-on sessions can help bridge this knowledge gap. Additionally, investing in enterprise-level AI tools with built-in security features, like GitHub Copilot for Business, can offer managers peace of mind and freedom for developers.

Addressing Security Concerns

One of the biggest challenges companies face with GenAI is ensuring data security. When developers upload proprietary code to unauthorized tools, they may inadvertently expose sensitive information. This can lead to severe consequences, from intellectual property theft to compliance violations.

Companies need to be proactive to minimize these risks. Approved tools with strong security protocols should be made available, and regular training should emphasize the importance of data protection. Rather than banning AI outright, organizations can adopt a collaborative approach that fosters trust and accountability.

Supporting Developers While Managing Risks

Developers are innovators by nature, and they’ll always find ways to optimize their workflows. Rather than stifling this creativity, companies should encourage it. Offering access to approved tools, rewarding responsible AI usage, and maintaining open communication can help align company goals with developers’ needs.

But the responsibility isn’t just on employers. Developers should advocate for the tools they need, share feedback about restrictive policies, and participate in training programs that help create a more supportive work environment. Ultimately, both sides benefit from working together to navigate the complexities of AI adoption.

Conclusion

Developers are leading the charge in GenAI adoption, even when it conflicts with company policies. This underscores an urgent need for organizations to rethink their AI strategies. Companies must transition from banning tools outright to enabling and guiding their responsible use, ensuring they keep pace with industry standards while minimizing risks.  

As AI continues to reshape how work gets done, the question isn’t whether to embrace it, but how to do so responsibly and effectively.

The post 49% of Developers Are Using GenAI Without Company Support (Survey) appeared first on HackerRank Blog.

The rapid rise of generative AI (GenAI) is reshaping software development, creating a pressing need for organizations to upskill their developer teams. Companies that adapt will thrive, while those who resist may fall behind. This brief guide will break down why it’s essential to upskill your developer teams for a GenAI future – and how you can get started today.

The Changing Landscape of Software Development

The software development field is undergoing a seismic shift thanks to advancements in AI. Approximately 75% of knowledge workers globally are using some form of GenAI to enhance their productivity and creativity. Tools like GitHub Copilot and ChatGPT have empowered developers to automate repetitive tasks, improve code quality, and accelerate development cycles. However, to fully leverage these technologies, developers need the right skills.

The Benefits of Upskilling Your Teams

1. Boosted Productivity: Research from the Association for Talent Development shows that organizations investing in continuous learning see a 24% increase in productivity. When developers are trained to effectively use AI tools, they can offload mundane tasks and focus on higher-level problem-solving, which boosts overall efficiency.
2. Increased Innovation: A report by PwC found that companies that prioritize employee training are three times more likely to innovate. Upskilling in GenAI allows developers to harness its capabilities to create new features and products, fostering a culture of innovation.
3. Improved Job Satisfaction and Retention: Employees who participate in upskilling programs report higher job satisfaction. According to LinkedIn’s 2023 Workforce Learning Report, 94% of employees would stay at a company longer if it invested in their learning. By upskilling your developers, you not only enhance their skills but also boost morale and retention rates.
4. Future-Proofing Your Workforce: As AI tools become more integrated into the development process, the demand for skilled developers will increase. A study by Gartner predicts that by 2027, 80% of the engineering workforce will require reskilling or upskilling to keep pace with technological advancements. By investing in training now, you ensure your team is prepared for future challenges.

Key Areas for Upskilling

To make your developer teams GenAI-ready, consider focusing on the following key areas:

• AI Literacy: Understanding the basics of AI and machine learning principles is crucial. Workshops and courses that cover AI fundamentals can help developers grasp how these technologies function.
• Prompt Engineering: As AI tools become more prevalent, knowing how to effectively interact with them is vital. Training in prompt engineering teaches developers how to ask the right questions and refine their outputs.
• Collaboration Tools: Familiarity with collaborative coding tools that integrate AI capabilities is essential. Upskilling teams in tools like GitHub Copilot ensures they can effectively use these resources in their workflows.
• Code Review and Quality Assurance: As AI generates more code, understanding how to review and refine that code becomes critical. Training in code quality practices will empower developers to ensure that AI-generated code meets high standards.

Steps to Set Up Developer Upskilling at Your Organization

Establishing an effective upskilling program involves a strategic approach. Here are the key steps to get started:

1. Assess Current Skills and Needs: Conduct a skills gap analysis to identify the existing capabilities of your development team and the skills necessary for future roles. Surveys and interviews can help pinpoint specific training needs.
2. Define Learning Objectives: Based on the assessment, outline clear learning objectives that align with your organization’s goals and the demands of GenAI technologies.
3. Select Training Methods: Choose a mix of training methods, such as online courses, workshops, coding bootcamps, and mentorship programs. Incorporating hands-on projects that utilize GenAI tools will help reinforce learning.
4. Implement a Learning Platform: Consider investing in a Learning Management System (LMS) to deliver, track, and manage training programs. This can provide employees with easy access to resources and facilitate ongoing learning.
5. Measure and Adapt: Regularly evaluate the effectiveness of your upskilling initiatives through skill intelligence metrics, feedback and performance metrics. Be prepared to adapt the program based on the evolving needs of your team and advancements in technology.
6. Encourage a Culture of Continuous Learning: Foster an environment that values ongoing education. Encourage developers to pursue their learning paths by providing time during work hours for training and promoting knowledge-sharing sessions.

Conclusion

In a world increasingly shaped by generative AI, upskilling your developer teams is a necessity. Investing in their development leads to increased productivity, enhanced innovation, and improved job satisfaction—all crucial for maintaining a competitive edge in the market. As the landscape of software development continues to evolve, equipping your teams with the right skills will empower them to harness the full potential of GenAI, driving your organization toward greater success.

HackerRank can get you ready with SkillUp

Whether you’re ready to start building your organization’s skills to become GenAI ready or you don’t know where to start, HackerRank can help. Our internal mobility & upskilling solution, SkillUp helps you get a valid assessment of the skills your organization has today so you can identify your strengths and gaps. 

With an objective view of your current skills, SkillUp can guide your dev teams to developing the critical skills needed to drive innovation and growth. All this delivered in an engaging fashion with badges & certifications awarded to validate the growth each developer has accomplished. 

If you’re interested in learning more, speak with a HackerRank expert today.

The post How and Why You Should Upskill Your Tech Teams for a GenAI Future appeared first on HackerRank Blog.

As AI rapidly advances, developers are faced with a fundamental choice: embrace a mindset of continuous skill enhancement, or risk being outpaced by those (and machines) who do. 

But there’s an underlying concern influencing how developers answer that question. 

If generative AI tools are going to streamline many programming tasks, how important will computer science skills actually be in the future?

We asked developers that very question in our 2024 AI Skills Survey. And an overwhelming 73% of developers said they expect core computer science skills to become even more important in an AI-driven world. Let’s explore why that’s the case.

AI Isn’t Replacing Skills – It’s Reinforcing Their Value

One of the most notable findings from our survey is that as developers interact with generative AI, they gain a renewed appreciation for core computer science principles. This trend reflects a growing sentiment across the industry: while AI can automate and assist with certain programming tasks, it still relies heavily on developers’ technical expertise to function effectively. 

Core skills – like debugging and optimizing code – not only ensure that AI-generated code is accurate but also enhance AI’s capabilities and reliability. This insight resonates with the World Economic Forum’s analysis, which stresses that technical fluency is essential for professionals who want to adapt and thrive as automation increases.

Core Skills Safeguard Quality and Innovation

In an AI-assisted workflow, core technical skills aren’t just about getting the job done—they’re critical for maintaining high standards. AI can streamline repetitive coding tasks and even generate complex code snippets, but human developers still play a vital role in assessing, refining, and ensuring code quality. 

A lack of understanding in core skills could lead to tech teams shipping AI-generated code that functions incorrectly, suffers from inefficiencies, or presents security vulnerabilities. Developers who invest in a deeper understanding of these core competencies will be better positioned to lead in innovation and quality assurance.

AI’s capacity to innovate is still inherently limited by the knowledge and creativity of its human operators. A developer who understands underlying principles and nuances is much more capable of steering AI tools toward unique and effective solutions, fostering innovation that purely AI-driven automation can’t achieve.

The Skill Gap – Active Skill Growth vs. Passive AI Reliance

For developers, AI offers powerful tools to streamline workflows and tackle repetitive coding tasks. However, a potential pitfall arises when developers rely too heavily on AI without deepening their own technical expertise. This reliance on AI can create a “skill gap” between developers who use AI to do most of the heavy lifting, and developers with deep expertise who merely leverage AI to enhance their work.

To stay competitive, developers should aim to integrate AI as a supportive tool in their learning process rather than a crutch. Developers who actively expand their technical skills—such as problem-solving and system design—are better equipped to handle complex challenges that AI alone can’t address. 

Developers who take this approach can position themselves for higher-value roles and career growth. Upskilling initiatives, like mastering advanced programming concepts, refining debugging skills, or learning how to train and fine-tune AI models, can provide a significant career edge.

AI and Technical Knowledge: A Collaborative Approach

Understanding AI’s limitations and strengths is just as important as mastering technical skills. Developers who lack a solid grasp of AI fundamentals might find themselves at a disadvantage. For example, developers should understand the intricacies of prompt engineering, which can help them troubleshoot AI-generated code, optimize AI-driven solutions, and create more resilient software systems.

This collaborative approach between human expertise and AI tools has far-reaching implications. For instance, developers skilled in data engineering and machine learning are better equipped to build, refine, and maintain AI systems that are ethical, secure, and efficient. The World Economic Forum further supports this idea, noting that skill diversification in AI, ethics, and technical areas is increasingly relevant for professionals aiming to future-proof their careers.

The Role of Continuous Learning and Upskilling in an AI Future

The fast-paced nature of AI means that today’s tools could become obsolete within a few years. To stay relevant, developers must actively pursue new skills in areas such as cloud computing, advanced mathematics, and cybersecurity, as well as new programming languages and frameworks as they emerge.

LinkedIn’s Future of Work report echoes this sentiment, stating that professionals who engage in lifelong learning are better positioned to adapt to technological shifts. Developers can leverage platforms like Coursera, edX, and LinkedIn Learning to keep their knowledge current. 

AI and Core Technical Skills: Partners, Not Substitutes

AI is not a replacement for developer skills but a tool that, when used effectively, enhances the value of these skills. While AI efficiently handles repetitive and straightforward tasks, it lacks the problem-solving depth, judgment, and adaptability that skilled developers bring to complex projects. AI can be a powerful ally, but it works best as a complement to the expertise of human developers who continuously hone their skills.

Together, AI and experienced developers can drive efficiency and innovation in ways that are both practical and impactful, underscoring the irreplaceable value of real technical knowledge in an increasingly automated world.

The post How Important Will Core Computer Science Skills Be in an AI Future? appeared first on HackerRank Blog.

Generative AI (GenAI) is transforming the tech landscape faster than any other technology in recent memory. For instance, ChatGPT reached 100 million users in just three months, 20 times faster than Google. Today, around 75% of knowledge workers worldwide use GenAI to boost productivity, save time, and spark creativity—and software development is at the forefront of this transformation.

How GenAI is Changing Software Development

In software development, GenAI is reshaping workflows, allowing developers to focus on creative problem-solving rather than mundane tasks. Developers now work alongside advanced AI models, which automate routine processes like generating boilerplate code, conducting code reviews, and automating testing. This human + AI collaboration significantly enhances efficiency.

With AI now embedded throughout the software development lifecycle (SDLC), companies need developers who can seamlessly integrate these AI tools into their workflows. And hiring practices need to evolve to meet this new reality.

The Rise of Specialized AI Agents

Basic automation is just the beginning of AI’s role in shaping software development. Advances in machine learning and natural language processing are giving rise to specialized AI agents capable of accelerating the development cycle, from idea to implementation. AI tools like GitHub Copilot can generate production-quality code—with developers retaining 80% of the AI-generated code—and AWS CodeWhisperer has demonstrated its power by drastically reducing software upgrade times, leading to impressive time savings and an estimated $260 million in annual efficiency gains.

These advancements allow developers to shift focus from repetitive tasks to more meaningful, high-impact activities. Imagine starting a workday by brainstorming solutions with an AI partner, generating initial code, reviewing it, and seamlessly moving to the next challenge—all while AI handles the grunt work.

The Developer as an Orchestrator

As AI handles more routine coding tasks, developers are transitioning into orchestrators—leading and guiding the overall development process. This new role demands creativity, critical thinking, and a solid understanding of core computer science (CS) fundamentals.  AI enhances productivity, but human expertise ensures the quality and reliability of the results.

The future of software development is one of partnership: developers working in tandem with AI, combining human ingenuity with the computational power of advanced models. The key skills of the future will be creativity, adaptability, and the ability to direct AI to achieve high-quality outcomes.

The Future of Hiring in Software Development

At HackerRank, we see big changes coming for software development hiring:

1. AI as the Primary Coder: AI will take on most coding tasks, allowing developers to focus on managing AI agents and solving complex problems.
2. Emerging Skills: As AI handles routine work, developers will need to sharpen skills like code review and prompt engineering, along with deepening their CS knowledge.
3. Role Consolidation: Traditional roles such as back-end, front-end, and QA may converge into a more unified “App Developer” role, with AI supporting the diverse tasks involved.
4. AI/ML Ubiquity: AI and machine learning will become deeply integrated across industries, increasing demand for developers who understand these technologies.
5. Increased Demand for Developers: Contrary to fears about AI replacing developers, we foresee efficiency gains leading to a greater demand for talented developers who can tackle new and complex challenges. The outdated notion that there’s a fixed amount of work—known as the lump of labor fallacy—doesn’t apply here.

Rethinking Hiring Practices

The rise of AI calls for a shift in how we hire developers. Companies need to move away from theoretical, algorithmic-style challenges and focus on real-world scenarios that reflect daily development tasks. 

HackerRank’s next-gen hiring approach does exactly this. Leveraging code repositories that simulate real-world environments helps us better assess a candidate’s ability to collaborate with AI, build features, and solve real issues.

Going a step further and integrating AI assistants into these environments enables candidates to receive real-time coding support while still being challenged to think critically and independently—mirroring the AI-augmented workplaces they’ll join.

Conclusion

Generative AI is reshaping software development, and as the industry evolves, so too must our approach to hiring. By focusing on real-world assessments, integrating AI into the hiring process, and emphasizing skills like creativity and problem-solving, companies can attract top talent and drive innovation in an AI-enhanced environment.

The future of software development will be driven by the partnership between human creativity and AI capabilities, pushing boundaries and expanding possibilities. As AI redefines what it means to be a developer, organizations need to adapt their hiring strategies to locate top-tier talent and stay ahead in this dynamic field.

HackerRank’s next-generation hiring solution

HackerRank is ready for the future of developer hiring. Our next-gen solution leverages code repositories and AI assistants to deliver real-world challenges that reflect the evolving realities of software development in an AI-first world. To learn more about how we can upgrade your hiring for the AI-driven era, download our whitepaper or speak with an expert today.

The post The future of developer hiring: Embracing AI and evolving hiring processes appeared first on HackerRank Blog.

With the rapid advancement of artificial intelligence, conversations about AI’s role in tech have shifted from distant hypotheticals to immediate realities.

There’s a growing chorus of questions about the implications of large language models (LLMs) like ChatGPT. How well can conversational AI code? And will AI replace developers?

But in contrast with the alarmism, a recent HackerRank survey found developers have an optimistic perspective on AI. 

Only 9% believe AI will replace them, with the majority expecting AI to enhance, rather than replace, their roles. This optimism among developers should guide hiring managers in their strategic planning, offering valuable insights into how AI can be an ally rather than an adversary in workforce development.

In this article we break down our survey’s findings in more detail, allowing hiring managers and developers to get an accurate picture of the tech industry’s future with AI.

Developers Are Embracing AI as a Tool, Not a Threat

Two years ago, the launch of ChatGPT and other generative AI tools stirred a wave of speculation about AI’s potential to disrupt jobs, particularly in software development. With AI’s ability to generate code snippets, troubleshoot, and optimize workflows, concerns were understandable. However, data today presents a clearer picture.

Developers are not overwhelmingly anxious about being replaced. Instead, they increasingly view AI as a valuable tool that complements their skills. In fact, 64% of developers feel AI will moderately or greatly increase their career opportunities. 

This shift in perception is pivotal for hiring managers, as it highlights developers’ willingness to leverage AI to boost productivity rather than seeing it as a competitor.

Career Prospects: An Optimistic Outlook

A significant portion of developers remains optimistic about their career prospects, with two-thirds of respondents predicting growth in job opportunities. In fact, 35% anticipate a large increase in future career opportunities, and another 29% expect a moderate increase. This confidence in future job market growth suggests that developers are preparing to adapt and thrive in an AI-enhanced environment, not retreat from it.

This insight is essential for tech hiring managers who are grappling with talent strategies. As the industry evolves, organizations that foster a positive view of AI will attract forward-thinking developers eager to integrate AI into their workflows. Companies that emphasize AI upskilling and career growth are likely to see lower turnover rates and greater loyalty, as developers recognize the potential for long-term career development.

The Role of AI in Enhancing, Not Replacing, Developer Skills

One of the key takeaways from this survey is that developers view AI as a way to enhance their work, not replace it. AI tools can handle certain aspects of coding, but they still rely on human insight, creativity, and problem-solving. 

AI excels at repetitive, rule-based tasks, freeing developers to focus on more complex, strategic projects. This augmentation rather than replacement should reassure hiring managers and provide clarity on AI’s true role within tech teams.

Rather than seeking candidates solely focused on manual coding, companies can look for developers who understand how to use AI to optimize workflows. This mindset aligns with the industry’s shift toward embracing hybrid skills that combine technical expertise with AI proficiency.

Building a Future-Proof Team: Why AI Training Is Key

Developers’ positive outlook on AI reflects a growing acceptance of AI as a critical skill. For hiring managers, this shift underscores the importance of building a future-proof team by prioritizing AI literacy and training. 

Consider incorporating AI training into onboarding or as part of ongoing professional development to  increase productivity and inspire innovation. With AI tools, developers can automate repetitive tasks, streamline code reviews, and enhance their problem-solving capabilities. 

AI training also fosters adaptability—a skill that will become increasingly valuable as AI technology continues to evolve. Hiring managers can stay ahead by ensuring their teams are equipped to learn and leverage the latest AI tools. This approach allows companies to remain agile, attracting talent that is both skilled and adaptable to the rapid technological changes ahead.

How Hiring Managers Can Leverage Developer Optimism

The positive sentiment among developers offers hiring managers a unique opportunity to leverage this optimism in recruitment and retention strategies. Companies that position AI as a productivity tool rather than a job-replacement risk are more likely to attract candidates who see value in continuous learning and adaptation. 

Emphasizing a forward-looking company culture—one that sees AI as an ally—can be a major draw for candidates. Companies that actively promote AI upskilling and professional development signal to candidates that they are committed to future-proofing their teams. This approach can also reduce turnover, as employees will feel secure in their roles and confident in the company’s dedication to career growth.

The Bottom Line: AI as a Catalyst for Growth

The AI Skills Survey underscores an important message for the tech industry: AI is not here to replace developers; it’s here to empower them. The majority of developers are embracing AI, expecting it to create opportunities and propel their careers forward. This optimistic outlook on AI’s role in tech offers hiring managers a roadmap for building resilient teams that thrive in an AI-driven world.

Hiring managers can respond to these insights by prioritizing AI training, fostering a culture of continuous learning, and positioning AI as a valuable tool rather than a threat. In doing so, they’ll attract and retain top talent, create a competitive advantage, and align their workforce with the future of tech.

The post Are Developers Worried AI Will Take Their Jobs? appeared first on HackerRank Blog.

Adopting a data-driven, strategic approach to skill acquisition and development can significantly boost a company’s performance. For tech teams, this translates to higher productivity, better innovation, and a more engaged workforce.

This isn’t just a theoretical concept – data from LinkedIn Learning shows that organizations with strategic skill development programs see increased retention rates and improved internal mobility.

In this article, we’ll explore ten essential elements to craft a winning tech skills strategy.

1. Skills Assessment and Inventory

Before you can improve, you need to know where you stand. Start by conducting a thorough skills inventory of your company. Use surveys, assessments, and interviews to catalog your team’s technical current skills. This helps you understand your strengths and identify gaps.

Next, project your future needs. What skills will be critical in the next 2-5 years? Compare your current inventory against these needs to pinpoint gaps.

How to do this:

1. Use Competency Matrices: These visual tools map out your team’s skills, making it easy to identify who has what skills and where there are gaps.
2. Leverage Skill Taxonomies: These are structured frameworks that organize and categorize skills into a hierarchical system. You can either consult existing taxonomies or create your own.
3. Conduct Regular Skills Assessments: Regular evaluations ensure you stay updated on your team’s current skill levels.
4. Interview Team Leads: Discuss upcoming technology needs with team leaders to align skill development with future projects.
5. Encourage Employee Self-Assessment: Promote a culture of self-improvement and accountability by having employees assess their own skills.

Tools and Resources:

• Skill Taxonomies and Matrices: HackerRank Roles Directory, Competency Matrix Template
• Skills Assessment Tools: Consider tools like HackerRank SkillUp for technical skill assessments for internal employees. 

2. Learning and Development Programs

Once you know the gaps, develop targeted training programs. Tailor these programs to address specific skills needed in your organization. This could include everything from coding boot camps to advanced machine-learning workshops.

How to do this:

1. Develop Role-Specific Training Programs: Tailor programs to specific roles, such as coding best practices for software developers and troubleshooting for IT support staff.
2. Encourage Participation in Online Courses and Certifications: Promote continuous learning through platforms offering relevant courses and certifications.
3. Schedule Regular Workshops and Seminars: Organize frequent training sessions to keep skills updated and relevant.
4. Collect Feedback from Participants: Gather feedback after each training session to make continuous improvements based on participant input.

Platforms and Tools:

These platforms offer courses and certifications that can help bridge skill gaps. Encourage your team to take advantage of these opportunities and integrate them into their professional development plans.

• Learning Programs and Online Courses: Coursera, Udemy, LinkedIn Learning, edX, Codecademy.
• Collect Feedback: Tools like SurveyMonkey can help you gather and analyze feedback.

3. Career Pathways and Progression

Clear career pathways motivate employees and provide a roadmap for their growth. Define what progression looks like for different roles within your organization. This could include moving from a junior developer to a senior developer and eventually to a tech lead or manager.

Promote from within whenever possible. Offering opportunities for role changes and advancements keeps employees engaged and reduces turnover. According to a Gallup report, organizations with high employee engagement are 21% more profitable.

How to do this:

1. Outline Career Trajectories: Define clear career paths for each role within your organization.
2. Create Advancement Opportunities: Link skill acquisition to potential promotions and role advancements.
3. Offer Role Changes: Provide opportunities for role changes to keep employees challenged and engaged.

Tools and Resources:

• Career Progression Frameworks: Stay informed about industry trends and forecast future skill needs. Resources like Gartner and Forrester provide valuable insights.
• Skill Mapping: Use skills mapping tools like SkillSurvey to align your recruitment efforts with future needs.
• Career Development Tools: Tools like Pathgather and Gloat provide personalized learning paths, social learning, and AI to match employees with internal opportunities. Both help tech companies manage skill development and internal mobility.

4. Talent Acquisition and Recruitment

When recruiting, look beyond immediate needs. Hire with the future in mind. What skills will you need in the next few years? This forward-thinking approach ensures your team is always prepared for upcoming challenges.

Diversity drives innovation. Ensure your recruitment practices are inclusive and aimed at building a diverse team. Diverse teams are proven to be more innovative and effective at problem-solving.

How to do this:

• Develop Future-Focused Job Descriptions: Write job descriptions with an eye on the skills and roles you will need in the next few years.
• Use Diverse and Inclusive Recruitment Practices: Implement recruitment practices that ensure a diverse and inclusive workforce.
• Partner with Universities and Boot Camps: Collaborate with educational institutions and boot camps to maintain a steady talent pipeline.

Tools and Resources:

• Recruitment Tools: Optimize Your Hiring Process
• Hiring Resources: University Recruiting Guide, Guide to Diversity Hiring.

5. Mentorship and Coaching

Mentorship is a powerful tool for development. Pairing less experienced employees with seasoned mentors can help accelerate their growth. This helps with skill development, fosters a culture of knowledge sharing, and builds a supportive community within your organization.

Encourage employees to learn from each other through peer learning sessions, hackathons, and collaborative projects.

How to do this:

• Establish Formal Mentorship Programs: Set up structured mentorship programs to guide employee growth.
• Host Regular Knowledge-Sharing Sessions: Organize sessions for employees to share expertise and learn from each other.
• Encourage Collaborative Projects and Hackathons: Promote team-based projects and hackathons to foster collaboration and innovation.

Tools and Resources:

• Mentorship Platforms: Together, MentorcliQ.
• Knowledge Sharing: Peer learning sessions, hackathons.

6. Performance Metrics and Evaluation

To ensure your skills strategy is working, set clear metrics for success. This could include the number of employees completing training programs, improvements in project delivery times, or adopting new technologies.

Regularly review these metrics and adjust your strategy as needed. Continuous improvement is key. Use performance data to refine your programs and address any new skill gaps.

How to do this:

• Define KPIs for Skill Development: Establish key performance indicators to measure progress in skill development.
• Track Progress and Adjust Programs: Monitor performance data and refine training programs as needed.
• Conduct Quarterly Reviews: Perform regular reviews of skill development metrics every quarter to ensure effectiveness.

7. Technology and Tools

Leveraging technology can help facilitate learning and enable employees to access training resources and track their progress. This could include Learning Management Systems (LMS) or custom internal tools.

AI and analytics can help personalize learning paths for each employee, ensuring they get the training they need when they need it. This tailored approach maximizes the effectiveness of your training programs.

How to do this:

• Implement an LMS for Centralized Learning: Set up a Learning Management System (LMS) to centralize and streamline access to training resources.
• Use AI to Personalize Learning Paths: Leverage AI tools to create customized learning paths based on individual progress and needs.

Tools and Resources:

• LMS Platforms: Moodle, TalentLMS.
• AI Tools: EdApp, Docebo.

8. Collaboration with Educational Institutions

Build relationships with universities and coding boot camps. These institutions can be valuable sources of new talent. Establish internships, co-op programs, and partnerships to create a steady pipeline of skilled graduates.

How to do this:

• Partner with Local Universities for Internship Programs: Establish internship opportunities with nearby universities to build a pipeline of skilled talent.
• Collaborate on Curriculum Development: Work with universities to shape curricula that meet industry requirements and future needs.
• Engage in Industry-Academic Projects: Participate in joint projects with academic institutions to benefit both industry and educational outcomes.

9. Employee Engagement and Retention

A culture of continuous learning keeps employees engaged and motivated. Recognize and reward employees who take initiative in their professional development. This fosters a positive, growth-oriented environment.

Celebrate achievements in skill development. This could be through formal recognition programs, promotions, or financial incentives. Recognition boosts morale and encourages others to pursue their own development.

How to do this:

• Implement Recognition Programs for Continuous Learning: Set up programs to formally acknowledge and reward ongoing employee learning and development.
• Offer Incentives for Skill Development Achievements: Provide rewards or bonuses for achieving specific skill development milestones.
• Create a Supportive Environment that Values Growth: Foster a workplace culture that prioritizes and supports personal and professional growth.

Tools and Resources:

• Employee Recognition Platforms: Bonusly, Kudos.
• Engagement Resources: Harvard Business Review: Employee Engagement.

10. Budget and Resource Allocation

Finally, a vital element of an effective skill strategy is to invest in your teams on their journey to grow their skills. This includes budgeting for courses, workshops, and tools and allocating time for employees to learn.

Skills development is not a one-time effort. Ensure sustained investment in these initiatives. Regularly review your budget and adjust as needed to keep up with changing industry demands and company goals.

How to do this:

• Allocate a Dedicated Budget for Training and Development: Set aside specific funds to support ongoing employee training and development programs.
• Ensure Continuous Investment in Skill-Building Initiatives: Commit to regular investment in skill-building activities to keep up with evolving industry demands.
• Regularly Assess and Adjust Budget Allocations: Continuously review and adjust your budget based on the changing needs of your organization and workforce.

The post Crafting a Winning Tech Skills Strategy: 10 Essential Elements appeared first on HackerRank Blog.

A well-conducted technical interview identifies candidates with the right skills and leaves a positive impression, enhancing the company’s reputation in the competitive tech job market. 

This article provides key strategies to sidestep common interview pitfalls and create a successful technical interview process to help you secure your top talent. Let’s dive in.

1. Prepare Thoroughly

Understand the Role

Before diving into interviews, it’s crucial to thoroughly understand the job requirements and key skills. This involves collaborating with the hiring team to define the essential skills and experiences required for the role. For example, if hiring a back-end developer, understand the specific programming languages, frameworks, and tools necessary for the job. Referencing an internal or third-party skills taxonomy can help with understanding the key skills for a role.

Prepare Relevant Questions

Create a mix of theoretical questions, coding challenges, and real-world problem-solving scenarios. Theoretical questions help assess the candidate’s foundational knowledge while coding challenges and real-world problems evaluate their practical skills and ability to apply their knowledge in real situations.

2. Create a Comfortable Environment

Set the Tone

Making candidates feel at ease from the beginning can significantly improve their performance. Start with a brief introduction about the company, the role, and what to expect during the interview. This helps set a positive tone and reduces initial anxiety. 

Minimize Stress

Interviews can be stressful, so it’s essential to minimize unnecessary pressure. Allow candidates to take short breaks and be patient during the interview. Use a conversational tone rather than a high-pressure interrogation style.

Provide Clear Instructions

Give clear and concise instructions for each part of the interview. Whether it’s a coding task or a system design question, ensure the candidate understands what is expected of them. Clear instructions help candidates focus on showcasing their skills rather than deciphering the question.

3. Focus on Problem-Solving Skills

Real-World Problems

Use technical problems that reflect real-world challenges the candidate will face on the job. This will test their problem-solving skills and give them a glimpse into the kind of work they will be doing. This approach will also serve   For example, instead of abstract algorithmic problems, use scenarios that involve debugging a piece of code or designing a small system.

Understand Their Approach

Evaluate the candidate’s approach to problem-solving rather than just the final solution. Observe how they break down the problem, their thought process, and their ability to adapt if they hit a roadblock. This provides insight into their analytical and critical thinking skills.

Interactive Problem-Solving

Incorporate pair programming or collaborative problem-solving sessions to observe teamwork and communication skills. This can be particularly effective in assessing how well a candidate will integrate with your existing team and handle collaborative projects.

Ask System Design Questions

The ability to architect engineering systems is a key skill for many technical roles. As such, system design questions are crucial for evaluating candidates’ ability to architect solutions. Ask candidates to design a system or component relevant to your business needs, such as a scalable web application or a microservices architecture. This assesses their high-level thinking and ability to handle complex projects.

4. Assess Soft Skills

Communication Skills

Evaluate how well the candidate communicates complex technical concepts. Good communication skills are essential for collaboration, especially in roles that require interaction with non-technical stakeholders. Look for clarity, coherence, and the ability to explain their thought process effectively.

Team Collaboration

Assess the candidate’s ability to work effectively within a team. This can be done through behavioral questions that explore their past experiences and observe their interaction during collaborative tasks. Teamwork is crucial for a harmonious and productive work environment.

5. Provide Constructive Feedback

Timely Feedback

Provide prompt feedback after the interview. Candidates appreciate timely responses, as they show respect for their time and effort. Whether they move forward in the process or not, timely feedback maintains a positive candidate experience. This is key, as a report by IBM found that a positive Experience makes candidates 38% more likely to accept an offer.

Specific and Actionable

Give specific and actionable feedback, highlighting both strengths and areas for improvement. Constructive feedback helps candidates understand their performance and how to improve, fostering a positive relationship even if they aren’t selected.

6. Continuously Improve the Process

Solicit Candidate Feedback

Gather feedback from candidates about their interview experience. This can provide valuable insights into what works well and what needs improvement. Use surveys or follow-up calls to collect this feedback.

Review and Iterate

Regularly review and iterate on the interview process based on feedback and outcomes. Identify patterns in candidate performance and feedback to make data-driven improvements. This continuous improvement approach ensures your interview process remains effective and competitive.

Train Interviewers

Ensure interviewers receive continuous training to stay up-to-date with best practices and techniques. Provide workshops or resources on effective interviewing skills, unconscious bias, and the latest industry trends. Well-trained interviewers are crucial for maintaining a high-quality interview process.

Conclusion

A successful technical interview hinges on thorough preparation and clear communication. Candidates value companies that respect their time and expertise. Tech companies can build a strong reputation and network by going beyond standard Q&A sessions to assess soft and technical skills, offering constructive feedback, and continually refining their interview processes.

The post How to Conduct a Good Technical Interview appeared first on HackerRank Blog.

In the fast-paced tech industry, keeping your development team at the cutting edge is not just beneficial—it’s essential. An effective upskilling program can bridge knowledge gaps, increase productivity, and keep your team motivated and engaged. Here are six best practices for creating a successful developer upskilling program:

1. Identify Skill Gaps

Before developing an upskilling program, it’s crucial to identify the current skill gaps within your team. By pinpointing specific areas that require improvement, companies can tailor their upskilling efforts to be more effective. This approach prevents resources from being wasted on irrelevant training and ensures that employees are gaining valuable, applicable skills.

To understand which skills need strengthening, use tools like skill assessments, performance reviews, and feedback from team leaders.

To identify skills gaps in your company, you should:

• Conduct Skills Assessments: Implement standardized tests or practical coding challenges to evaluate your employees’ current skill levels.
• Analyze Performance Reviews: Review past performance evaluations to identify recurring areas where employees fall short.
• Gather Feedback From Team Leaders: Conduct interviews or surveys with team leaders and managers to get their insights on where their team members could improve.
• Monitor Industry Trends: Stay updated on the latest trends and technologies in the industry to identify new skills that your employees may need.

Questions to Ask:

• What are the critical skills required for our current and future projects?
• Which skills are our top-performing employees excelling in, and how do others compare?
• Are there any recurring issues or gaps highlighted in recent project post-mortems?
• What new technologies or methodologies are emerging in our field that we need to adopt?
• How do our current skills compare to those of our competitors?

2. Offer a Variety of Learning Resources

Different people learn in different ways, so it’s crucial to offer a variety of learning resources. Some developers might prefer hands-on coding exercises, while others might benefit more from video tutorials or reading materials. Consider incorporating online courses, workshops, hackathons, coding bootcamps, and access to technical books and journals. Providing diverse learning options ensures that every team member can find resources that suit their learning style.

3. Adopt Microlearning Strategies

Microlearning prevents cognitive overload and improves knowledge retention. It helps avoid the pitfall of overwhelming employees with extensive training sessions that are hard to digest.

Break down complex topics into smaller, more digestible chunks of information. Microlearning modules can be delivered in short bursts, making it easier for employees to fit learning into their busy schedules.

• Develop Microlearning Modules: Break down complex topics into short, focused modules. Use tools like Articulate or Adobe Captivate to create engaging microlearning content.
• Leverage Mobile Learning: Make microlearning content accessible on mobile devices so employees can learn on the go.
• Regular Updates: Keep microlearning modules up-to-date with your industry’s latest information and trends.

4. Implement Gamification Techniques

Gamification boosts motivation and participation in training programs. It helps avoid the pitfall of turning training into a chore for employees. Incorporate gamification elements like leaderboards, rewards, and challenges into your training programs. Gamification increases engagement and makes learning more enjoyable.

• Incorporate Game Elements: Add leaderboards, badges, and challenges to your training programs. 
• Design Competitive Tasks: Create team-based competitions and rewards to foster a healthy competitive environment.
• Track Progress: Use gamification to track employee progress and provide instant feedback.

5. Incorporate AI and Machine Learning Training

Artificial intelligence (AI) and machine learning (ML) are transforming software engineering and development as we know it, making them crucial areas for developer upskilling. Providing training in these fields can open up new opportunities for innovation and problem-solving. Here’s how to effectively incorporate AI and ML into your upskilling program:

• Start with the Basics: Introduce the foundational concepts of AI and ML, including algorithms, data structures, and statistical methods. Ensure that developers understand the principles behind these technologies before diving into more advanced topics.
• Practice with Generative AI: Upskilling developers in coding with generative AI can significantly enhance their productivity and creativity. 
• Provide hands-on experience: Encourage practical, hands-on experience with AI and ML tools and frameworks such as TensorFlow, PyTorch, and Scikit-Learn.
• Discuss Responsible AI: Training should also cover the ethical implications and responsibilities associated with AI development. Developers should understand issues such as bias in AI, data privacy, and the societal impact of AI technologies.

6. Foster a Culture of Continuous Learning

A culture of continuous learning keeps employees adaptable and ready to face new challenges. It helps avoid the pitfall of stagnation and the inability to keep up with technological advancements.

Encourage an engineering culture where continuous learning is valued and supported. Provide resources such as online courses, workshops, and access to learning materials to promote ongoing skill development.

• Promote Lifelong Learning: Encourage employees to pursue continuous learning through regular communication and incentives.
• Provide Learning Resources: Offer access to online courses, workshops, and a digital library of learning materials.
• Mentorship Programs: Establish mentorship programs where experienced employees can guide and support less experienced team members.

The post 6 Best Practices for Creating a Developer Upskilling Program appeared first on HackerRank Blog.