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Abhishek Gupta on Why Adaptability will Define the Next Generation of AI Professionals
Abhishek Gupta is the Chief Business Officer at Learnbay, a Bengaluru-based edtech company specialising in data science and AI education. An engineer turned business leader, he began as a backend developer and rose to lead growth — blending hands-on technology depth with a builder’s instinct for scaling real learning outcomes.
Abhishek Gupta is a technologist and business leader whose career reflects a rare ability to move between building products and building businesses. An Electronics and Communication Engineering graduate from West Bengal University of Technology (2014), he began his career as a developer in the fintech space, where he learned to ship reliable software under the pace and pressure of an early-stage startup.
His early years spanned domains that shaped his engineering foundation. At a Bengaluru telecom company, he worked on video-conferencing technology built to meet government protocols and compliance standards. He then moved into a fast-growing solutions startup, contributing across a range of client projects and sharpening his ability to deliver in ambiguous, resource-constrained environments.
In 2017, Abhishek joined Learnbay — an edtech company focused on data science, AI, and machine learning education — as a backend developer. Over the next few years he grew with the company, first strengthening its technology backbone and then teaching Python and data science to learners after work hours. That time in the classroom proved formative: it gave him a direct line into student concerns, learning outcomes, and the operational realities of the business.
His curiosity about how businesses actually work led him from engineering into business development, and eventually to the role of Chief Business Officer. Today he leads Learnbay’s growth and business strategy, keeping its curriculum, tools, and content aligned with a technology landscape that shifts constantly.
What distinguishes Abhishek is his dual fluency. He understands code and classrooms, systems and students, technology and the economics that sustain it. As a leader in a bootstrapped company, he has learned to grow deliberately — prioritising learner outcomes and sustainable execution over noise. His path from developer to CBO stands as a testament to curiosity, adaptability, and a willingness to keep learning.
Could you briefly tell us about your professional journey and what inspired you to join Learnbay?
My journey has been anything but linear, and I think that has been its biggest strength. I started as a developer in fintech, then worked on video-conferencing technology in telecom, and spent time in a solutions startup handling varied client projects. Each role taught me to build under real constraints. I joined Learnbay in 2017 as a backend developer, and what kept me here wasn’t a single moment — it was seeing the impact up close. I began teaching Python and data science after work hours, and sitting with learners showed me exactly where careers get stuck and how the right training changes lives. That pulled me toward the business side; I wanted to understand the engine driving it all — how a learning company sustains itself, scales, and stays relevant. That curiosity shaped my path from engineering to business development, and eventually to Chief Business Officer.
What is Learnbay’s vision for transforming data science, AI, and cybersecurity education?
Our vision is simple: make advanced tech education genuinely industry-ready, not just academically complete. In fields like data science, AI, and cybersecurity, the gap between what is taught and what employers need widens every year. We want to close that gap — with curriculum built around real business problems, mentorship from working professionals, and hands-on projects that mirror the job. The goal isn’t to hand out certificates; it’s to make a working professional truly employable in a domain that evolves faster than most classrooms can keep up with.
How do you ensure that Learnbay’s programmes remain aligned with evolving industry needs?
We treat curriculum as a living product, not a fixed syllabus. Three things keep it current. First, our mentors are practitioners from the industry, so what they teach reflects what they actually use at work. Second, we update content continuously — new tools, frameworks, and topics like Generative AI and agentic AI get folded in as they mature, not years later. Third, we listen to the market and our learners; hiring trends and placement feedback directly shape what we add or retire. In a bootstrapped setup, that discipline matters even more — we can’t afford to teach anything that won’t help someone get hired.
What role do AI and data-driven technologies play in shaping the future of education?
A significant one — and it cuts both ways. AI is becoming a core subject learners must master to stay relevant, so it shapes what we teach. But it is also changing how we teach. Data helps us understand where learners struggle, personalise support, and improve outcomes instead of guessing. AI can handle practice, doubt-solving, and feedback at scale. What it can’t replace is mentorship, accountability, and the human push that gets someone across the finish line. To me, the future of education is AI handling the scalable parts so humans can focus on the parts that actually change careers.
What has been one of the most important milestones in your leadership journey so far?
The most meaningful one wasn’t a title — it was the move from writing code to owning the business. Being trusted to step out of engineering and into business development, and later into the CBO role, was a turning point. It meant learning an entirely new discipline mid-career: revenue, growth, operations, and strategy. Getting that opportunity, and proving I could grow into it, taught me that potential is worth betting on. That belief now shapes how I lead and how I look at the people on my own team.
What are the key challenges you see in bridging the gap between academic learning and industry requirements?
The core challenge is speed. Academic content moves in years; industry moves in months, so by the time a fixed syllabus is printed, the tools have often changed. The second challenge is depth versus application — learners often know concepts but haven’t applied them to a messy, real-world problem, which is exactly what employers test for. And third, there is a mindset gap: industry rewards ownership, communication, and problem-solving under ambiguity, not just correct answers. Bridging all three needs current content, real projects, and mentors who have done the actual work.
What advice would you give aspiring professionals looking to build a career in data science and AI?
Build depth before chasing titles. The field is noisy right now, and it is easy to collect certificates without real skill. Pick the fundamentals — statistics, programming, problem-solving — and get genuinely good at them, because tools will keep changing but the fundamentals won’t. Work on real projects you can defend in an interview, not just tutorials you followed. Stay curious about the business context, not only the model; the professionals who grow fastest are the ones who understand why a problem matters, not just how to solve it. And don’t wait to feel ready — start, ship, and learn in the open.
What are your future plans for Learnbay, and what can we expect from the organisation in the coming years?
Our focus stays on outcomes and relevance. In practice, that means deepening our AI and Generative AI offerings as the field matures, expanding into adjacent, high-demand skills, and continuing to strengthen the mentorship and placement support that make the learning stick. We will keep growing deliberately — we’re a bootstrapped company, and that keeps us honest about building something sustainable rather than chasing scale for its own sake. If we keep helping working professionals make real career transitions in a fast-moving industry, the growth follows. That’s the plan.
