Why Some Young Professionals Are Saying ‘No’ to IITs and Choosing Masters’ Union for AI Programmes

For generations of engineering graduates in India, the IIT system has represented more than an academic pathway. It has functioned as the country's most trusted signal of technical potential: a rigorous, entrance-ranked filter that promised, and largely delivered, access to the best peer networks, research environments, and career trajectories in the profession. The institutions earned that reputation through decades of genuine output. Engineers trained at IITs built careers that validated the selection process, and the signal compounded with every successful cohort that followed.

That signal remains powerful. What is changing is the question being asked alongside it.

India's AI economy is producing a new and specific demand: professionals who can build, deploy, and operationalise intelligent systems in production environments. It is a demand that entrance examinations were never designed to predict and that postgraduate research curricula were never structured to produce at scale. For a growing cohort of young engineering graduates, this gap is reshaping a calculation their families and institutions assumed was already settled. The path to the most consequential roles in the AI economy does not run only through the institutions that have historically defined engineering excellence in India. For some, it is running through somewhere else entirely.

What the IIT Path Was Built For

The traditional value of elite engineering education in India rests on foundations that remain genuinely strong. The peer ecosystems at top colleges of India for engineering produce a density of talent that shapes careers long after graduation. The research infrastructure and brand equity of these institutions have made them the reference point for technical excellence in the country for generations.

What they were not built for is the specific demand now emerging at the frontier of the AI economy. According to Gartner's 2026 Hype Cycle for Agentic AI, more than 60% of organisations expect to deploy AI agents within the next two years. LinkedIn's AI Labour Market Report 2026 shows AI hiring in India has surged by nearly 60% year-on-year. The roles driving that surge are not research positions or academic appointments. They are implementation roles: engineers who can build AI agents, deploy RAG pipelines, manage LLMOps workflows, and ship production-grade AI systems inside real business environments.

A postgraduate seat at one of the top colleges of engineering in India produces exceptional researchers and engineers. It was designed to. But hiring conversations at the frontier of India's AI economy are now asking for something the traditional pathway was never structured to produce: professionals who combine AI and ML engineering depth with product and business understanding. Emerging roles such as Forward Deployed Engineer, AI Product Manager, and AI Strategy Consultant demand far more than technical expertise. They require the ability to translate AI into deployable products, work closely with customers and business teams, and solve enterprise problems at scale. A portfolio of production-grade AI systems, built and reviewed against industry standards, is increasingly becoming as valuable as academic credentials. That is a capability many traditional M.Tech and postgraduate programmes offered by IITs were not originally designed to develop.

What Young Professionals Are Actually Being Asked For

The gap between pedigree and proof of work is landing hardest on the generation entering the workforce right now. Fresh graduates and early career professionals from strong engineering backgrounds are discovering that the market has shifted its primary hiring signal faster than most educational pathways have shifted their curricula.

According to a TeamLease report, only one qualified engineer is available for every 10 open GenAI positions in India, with a projected 53% talent shortfall by 2026. The scarcity is not of people with strong academic records or recognised AI degrees. It is for people who can demonstrate implementation capability. Professionals with expertise in AI engineering and LLMOps frequently command salaries 30 to 80% higher than peers in conventional development roles. A 2025 NITI Aayog report estimates strategic upskilling could create nearly 4 million new roles in India over the next five years, skewed sharply towards execution.

This shift is increasingly shaping real education decisions. Janmejay, for instance, chose Masters' Union over an admission offer from IIT Delhi's MBA programme, after also evaluating postgraduate options at IIT Madras and the University of Birmingham. His decision reflected a different priority. Rather than moving directly into management, he wanted to first build a stronger technical foundation in applied AI. He also found the foreign university programmes considerably more expensive. Masters' Union's rapidly evolving, industry-aligned curriculum made it the pathway that best aligned with the capabilities he wanted to develop before pursuing leadership roles later in his career.

For young professionals who spent years preparing for entrance exams and now face a market asking what they have shipped, the realisation is clarifying. Academic excellence and implementation readiness are not the same signal. Increasingly, it is the second that determines what happens in the interview.

Why Masters' Union Is Becoming the Answer to a Question IITs Were Never Asked

Masters' Union did not set out to replicate the traditional postgraduate engineering model. Instead, its PG Programme in Applied AI and Agentic Systems asks a different question altogether: what should AI education look like when employers increasingly hire for proof of work of deployed systems instead of academic credentials?

The answer is reflected in the programme's structure. Across a full-time, 15-month curriculum, students first develop strong AI and machine learning foundations before progressing into specialised pathways in AI Product, Advanced AI/ML & Systems, or AI Entrepreneurship to prepare them for not only AI/ML engineering roles but also for the roles such as FDE, AI PM, AI Strategy Consultant, and many more which require a grip on product and business along with the AI/ML engineering depth . Every academic term culminates in a production-ready deployment, enabling graduates to leave with six AI systems spanning autonomous AI agents, enterprise AI deployments, Retrieval-Augmented Generation (RAG) pipelines, knowledge graphs, fine-tuned frontier and open-source models, and agentic AI applications. In the final terms, students can also pursue frontier work by deploying Small Language Models (SLMs), building Physical AI and multi-agent enterprise systems, or developing AI ventures of their own.

That emphasis on building shaped Garvit's decision. Despite offers from IIT Gandhinagar, Nirma University, and Adani Institute, and after completing an AI programme at IIIT Hyderabad, he chose Masters' Union because the learning model aligned more closely with the kind of engineer he wanted to become, one whose capabilities could be demonstrated through deployed products rather than examinations.

The programme continues to evolve alongside the industry it serves. Curriculum updates are incorporated every academic term with inputs from experts at Google, Microsoft, Amazon, IBM, and PayPal, supported by collaborations with organisations including PwC and Rabbit AI. Outside formal coursework, students work within a live builder ecosystem featuring mentorship from more than 200 CTOs, founders, and AI practitioners, alongside build studios, hackrooms, practitioner sessions, collaborative product sprints, and real-time engagement with frontier technologies.

The Question Underneath the Choice

Among top colleges for AI in India now emerging as genuine pathways into the AI economy, the programmes gaining traction share a characteristic that has nothing to do with entrance ranks: they are built around what the market is hiring for, not what academia has traditionally rewarded.

The young professionals making this choice are not dismissing what IITs represent. They are asking a more precise question: for the specific future I am trying to build, which pathway actually prepares me for the work?

Pedigree and proof of work are not opposites. But for a generation entering the AI economy at its most consequential inflection point, the sequence is shifting. As Razorpay's Talent Acquisition team recently observed, in the AI era, proof of work is becoming more valuable than a CV, reflecting a hiring environment where demonstrated execution increasingly outweighs credentials alone. The broader labour market is moving in the same direction. Mercer's 2025/2026 Skills Snapshot found that 65% of employers have already adopted skills-based hiring, signalling a growing preference for demonstrated capabilities over qualifications.

Masters' Union reflects that shift through a full-time, on-campus model that combines engineering, product, and business, evolves continuously with the pace of AI, and requires every student to graduate with six production-grade AI systems. For young professionals weighing traditional prestige against execution, the appeal lies in this combination few programmes currently offer, and one that increasingly aligns with how the AI job market itself is evolving.