Why did you decide to become a mentor?
Becoming a mentor felt like a natural extension of my long-standing commitment to sharing knowledge and supporting others in the ML community. Throughout my career, I’ve consistently mentored peers and junior colleagues, helping them grow technically, navigate career decisions, and solve real-world ML problems. I see mentoring, teaching, and writing as parallel efforts that serve the same goal: making machine learning more accessible, practical, and impactful.
I regularly share insights through my personal blog and on TowardsDataScience, covering topics like model design, data-centric development, and deployment lessons. I also teach Deep Learning and Generative AI through bootcamps and eLearning platforms, focusing on upskilling as well as getting folks interview-ready. These educational activities help me distill complex concepts into clear, actionable guidance.
My book Sculpting Data for ML reinforces this philosophy; it captures hard-earned lessons on building robust ML systems and reflects my belief in empowering others through structured, real-world insights. Mentoring is a personal, high-impact way to extend that mission and stay connected to the evolving needs of the ML community.
How did you get your career start?
I got my start in Machine Learning through a blend of strong academic training and a deep curiosity for solving real-world problems. I earned a Master of Science in Machine Learning from UC San Diego, following an undergraduate degree in Computer Science from Thapar University in India. During that time, I actively pursued research internships and teaching assistantships, which helped me bridge academic theory with hands-on application early on.
Along the way, I led a series of niche research projects, ranging from clothing fit prediction to sarcasm and spoiler detection. I was drawn to these problems not just for their technical depth, but because they had the potential to improve user experience on the web. I published my work in top-tier conferences and shared learnings through blog posts, which strengthened my ability to communicate complex ideas and connect with the broader ML community.
This foundation led naturally into roles at companies like Amazon and Twitter, where I scaled these learnings to production-grade systems. I worked on building low-latency, large-scale ML systems and led deep learning–powered personalization efforts. Looking back, each step from academic research to applied industry work was part of a deliberate path toward mastering the domain and delivering real-world impact through AI / ML.
What do mentees usually come to you for?
Mentees come to me with a wide range of goals and challenges across the Machine Learning landscape. I work with aspiring ML professionals, early-career engineers, experienced developers transitioning into ML, researchers, academics, and enthusiasts alike. My mentorship is tailored to each individual, combining technical skill-building with career strategy and industry insights.
Some of the most common areas I support include:
- Personalized guidance aligned with specific career paths and aspirations
- Mock interviews for AI / ML roles, informed by my experience conducting hundreds of interviews
- Portfolio and project advice to showcase impactful, relevant work in AI / ML domain
- Strengthening AI / ML fundamentals and research thinking
- Strategies for gaining hands-on experience and breaking into the field of AI / ML
- Navigating the AI / ML job market, with insights into hiring trends and expectations
- Resume reviews with targeted, actionable feedback for AI / ML roles
Mentees often highlight the real-world value of our sessions, noting how the advice translates directly into increased clarity, confidence, and results. Drawing from my time at Twitter, Amazon, and a high-growth startup, I bring a practical, insider perspective that helps them thrive in today’s competitive AI / ML landscape.
What's been your favorite mentorship success story so far?
While I don't have one single favorite success story, each mentee's journey is unique and rewarding. I've had the privilege of helping people achieve significant milestones that are incredibly fulfilling to witness. Success for me often goes beyond just an external achievement; it's about the mentee's personal and professional growth and their empowerment to navigate the ML landscape confidently.
I've had the satisfaction of helping numerous individuals clear challenging AI / ML interviews at top-tier tech companies (often referred to as FAANG or similar), guiding them through technical complexities and system design challenges. It's also been incredibly rewarding to help younger learners, including school kids, get started with AI, sparking their curiosity and providing foundational knowledge. Furthermore, I've guided many talented individuals looking to transition into AI from other fields, helping them identify transferable skills, fill knowledge gaps, and strategically position themselves for new opportunities.
What are you getting out of being a mentor?
Mentoring provides both intrinsic fulfillment and professional enrichment that deeply align with my broader goals in the ML field. I’ve always found genuine satisfaction in helping others grow, whether it’s guiding someone through a technical challenge or supporting them in making pivotal career decisions. Watching mentees gain confidence, navigate the ML landscape more effectively, and achieve their goals is one of the most rewarding aspects of what I do.
At the same time, mentoring sharpens my thinking. Explaining nuanced concepts, answering diverse questions, and helping others troubleshoot real-world problems continuously challenges me to refine my understanding and improve how I communicate. It keeps me intellectually agile and ensures that my knowledge stays current and grounded in practical relevance.
Having conducted hundreds of ML interviews, I also bring a unique perspective to common gaps in preparation, misunderstandings about industry expectations, and the traits that differentiate successful candidates. Mentoring gives me a direct avenue to share these insights in a way that has immediate impact.
Beyond the one-on-one interactions, mentoring helps me stay connected with the evolving challenges practitioners face, fresh perspectives from those entering or transitioning into AI / ML, and broader trends shaping the talent landscape. It complements my other community contributions, like reviewing for top AI conferences and writing about ML best practices, by offering a more personal, hands-on way to support the next generation.
Ultimately, mentoring reinforces my belief that shaping strong, thoughtful AI / ML practitioners contributes to the long-term health, quality, and integrity of the field. Seeing mentees succeed is not only fulfilling, it’s a meaningful validation of my approach to leadership, education, and community-building.