An AI-Forward Approach to Mentoring the Future of Tech

An AI-Forward Approach to Mentoring the Future of Tech

For over a decade, Srikala Gudavalli has built her career on precision. When she started at EPAM as a software and test automation engineer, her daily focus was on removing redundancy, scaling solutions and ensuring quality. But as her passion for teaching grew, she discovered she wanted to optimize something even more complex than code: human capability. Now, as a Chief Talent Development Specialist at EPAM, Srikala mentors hundreds of junior engineers each year. By combining her engineering background with an AI-forward approach, she builds personalized learning ecosystems at scale that demonstrate how tech talent grows. Her journey illustrates how AI can transform education and how EPAM helps its people continuously pivot, learn and engineer the future.

The Human Challenge of Scaling Mentorship 

Transitioning from a highly technical role in test automation to a people-focused position in learning and development might seem like an unusual pivot. For Srikala, it was an intentional progression that reflects both her commitment to educational impact and EPAM’s culture of internal mobility.

The mindset of optimization that she employed as an engineer was a perfect fit for her new role in education. When tasked with mentoring hundreds of recent graduates, she soon recognized the core challenge: how to scale a high-quality mentorship experience without losing the personal, human touch. But before she could introduce an AI-forward solution, Srikala had to navigate a challenging reality familiar to many talent development leaders.

“The biggest issue was feedback latency,” Srikala explains. “The time between making a mistake and someone catching it could sometimes be two or three days. By then, the learner had moved on and internalized the wrong method. So then, you are not just correcting a mistake — you are trying to undo a habit.”

EPAM recognized this challenge and encouraged Srikala’s team to ideate solutions and implement them. Realizing the importance of preparing the next generation for the future, her team began to work on building a proactive learning system.

Building an AI-Forward Learning Architecture

Bringing AI into her work started small. Srikala began by automating routine emails, building simple agents for common queries and creating quick quizzes to support core learning modules. 

But as she explored the technology further, she realized the greater potential impact. AI can summarize complex technical content and create adaptive learning paths tailored to each person’s pace, providing real-time guidance based on a student’s specific challenges and making the learning experience more personal and effective. 

“Instead of delivering the same experience to everyone, I can design customized experiences and provide real-time feedback,” Srikala says. She explains how the team worked with internal and external tools to shift away from a reactive learning system and built a proactive, three-layer approach:

Curating tailored learning paths: CodeMie, EPAM’s AI-native multi-agent platform designed to streamline the Software Development Life Cycle, changed how Srikala’s team designs education. Beyond organizing content, it helps educators create technical tasks and replicate code across hundreds of accounts, streamlining the SDLC and saving instructors hours of manual work.

Simulating production environments: For project-based education, Srikala’s students must work in realistic, mock production environments. Tools like EPAM EliteA™ help design test automation workflows and generate scripts, bridging academic theory and practical application.

Providing instant feedback: Instead of waiting days for a mentor to review potential errors, the system now provides junior engineers with immediate, structured responses so they can self-correct in real time. With Autocode AI, they receive instant, relevant feedback as soon as they submit their code.

While these tools were helpful, Srikala stresses that tech is only part of the solution.

“The greatest value was the method we designed for the tooling,” she says. “When engineers in the program accept or reject an AI suggestion, they must explain why. This transforms a tool into an active critical thinking exercise for engineers.” To sum up, she adds, “We didn’t automate learning; we engineered the conditions for deeper thinking.” 

A Return to Teaching 

A common concern around AI in education is that it could replace human instructors, but Srikala’s experience proves the opposite. By offloading standard code reviews and administrative tasks, she and fellow educators can elevate their impact.

The team also planned a safeguard to mitigate dependence on AI. If the platform flags that automated suggestions are being accepted without strong written reasoning, a mentor can step in. Rather than discouraging use of the tool, the mentor can ask for the engineer’s analysis of the suggestion. 

Srikala notes that this approach can change students’ work almost immediately. “It allows us to get back on track, move past basic mistakes and start teaching intent. AI provides data that tells us exactly when a human conversation is needed,” she explains.

But while AI can provide data, humans must still provide context. If a junior engineer’s output quality drops, Srikala notes that the cause could be a confidence issue, a personal struggle or a team dynamic, all of which are nuances that AI cannot uncover or understand. 

“Culture is still a very human project,” she says, noting that accountability and growth still require a mentor to invest in a learner’s success. “AI can support it, but we have to create it.”

Building Confidence Through Experimentation 

As the tech industry evolves, Srikala now acts as a strategic guide by helping professionals overcome their initial doubts and focus on transferable skills. Because AI systems require well-structured context to perform, engineers who write clearly and annotate their reasoning will stand out.

EPAM’s environment makes this kind of professional evolution possible. Backed by institutional support and a culture of continuous learning, Srikala can test hypotheses across hundreds of learners and gather educational data in real time. 

For those feeling unsure about where to begin their own journey, her advice is straightforward: adopt an experimental mindset. “Think of AI as a journey, not a destination,” she says. “Pick up a tool, experiment and learn from your failures. Be curious and let your learning build naturally. Curiosity, when disciplined, is a professional credential.” 

Ready to engineer your future? Explore more stories from EPAMers around the globe and see how AI-forward learning is helping shape what comes next at epam.com/careers.