The Pune Hiring Data Tells a Story We Should Not Ignore
This post reached 9,700 impressions on LinkedIn — which suggests the observation resonates beyond my own experience. I have expanded it here with the full data, the structural explanation, and what I believe needs to change.
I ran a hiring search recently across Pune — one of India's tier 1 technology markets, home to the engineering centres of global banks, product companies, and technology services firms.
The results were instructive. And if you are a technology leader thinking about AI deployment at enterprise scale, they should concern you.
What the data showed
Across four parallel searches over approximately one month:
- Scrum Master — 70 qualified profiles available within two hours
- Hands-on DevOps Manager — zero profiles in a full month of active search
- Hands-on Data Engineering Manager — one profile found after four days
- DevOps Engineer with platform engineering depth — zero profiles in a month
Let me be precise about what I mean by "hands-on." I am not looking for managers who once wrote code and now manage people who do. I am looking for leaders who still operate at the technical level — who can review a Terraform module, debug a Spark job, diagnose a Kubernetes cluster issue — while also managing delivery and developing their teams.
That profile — technically deep, delivery-capable, and leadership-ready — is effectively absent from the Pune market at the moment I ran this search.
The coordination profile — certified, process-fluent, stakeholder-ready — is in surplus.
How we built this imbalance
This did not happen by accident. It is the predictable outcome of a decade of incentive structures that rewarded coordination credentials over technical depth.
A Scrum Master certification takes days to acquire. A PMP takes weeks of study and an exam. Both historically commanded strong compensation relative to the time invested. The return on effort was clear and fast.
Building genuine platform engineering depth — the kind that lets you architect a data lake, manage a Kubernetes fleet, or design a CI/CD pipeline that handles regulated workloads — takes years of hands-on work. It requires making mistakes on real systems, recovering from real failures, and accumulating judgment that cannot be compressed into a course.
The Indian technology education and career development ecosystem made the rational choice easy and made it consistently, at scale, for a decade. We now have the talent market that those incentives produced.
I understand the value of coordination. In my career managing up to 450 professionals across global delivery programmes, program managers and delivery leads were essential. Coordination at that scale is genuinely hard and genuinely valuable.
But coordination without the engineering depth to execute on what is being coordinated is a liability, not an asset. It creates the appearance of delivery capacity without the substance of it.
Why this matters right now — the AI inflection point
We are in the earliest stages of deploying AI at enterprise scale. This is not a speculative claim. The investment is committed. The roadmaps are set. The boards have approved the budgets.
What has not been honestly assessed in most organisations is whether the talent pipeline matches the ambition.
AI systems in banking, healthcare, and critical infrastructure are not dashboard tools. They are infrastructure — data pipelines, model serving platforms, feature stores, monitoring systems, retraining workflows, access control layers, audit trails. They need to be architected for the regulatory environment they operate in. They need to be productionised to the reliability standards that regulated industries require. They need to be secured against adversarial inputs and data poisoning. They need to be scaled without degrading the risk controls that surround them.
These are not coordination activities. They require engineers who can think in systems, write code that handles failure gracefully, and understand the domain consequence of every technical decision they make.
The Scrum Master cannot deliver this. The builder can.
When I look at the hiring data from Pune and ask whether we have the talent pipeline that the AI ambition requires — the honest answer is no. Not yet. Not at the ratio we need.
What needs to change
Three things, from my perspective as someone who hires, builds teams, and cares about the long-term health of the engineering profession:
Career incentives need to rebalance toward depth. The compensation premium for deep technical contributors needs to be as visible and credible as the path through coordination into management. Right now, the fastest route to a senior title and strong compensation in most Indian technology organisations runs through people management, not technical excellence. That routing needs to change.
Engineering education needs to weight practical systems experience. The gap between what computer science programmes produce and what enterprise engineering actually requires is not new. But the AI deployment wave makes it acute. Building, breaking, and recovering real distributed systems cannot be replicated in classroom environments. Apprenticeship models, open source contribution pathways, and industry partnership programmes that give engineers access to real infrastructure problems need serious investment.
Organisations need to be honest about what they are actually building. Many organisations claiming to deploy AI are deploying proof of concepts behind coordination-heavy programme structures. The gap between the roadmap and the engineering reality is papered over by process. When the proof of concept hits the productionisation wall — as it inevitably does — the absence of builder depth becomes suddenly, expensively visible.
The question I am sitting with as a technology leader is this: are we creating the talent pipeline that matches the technology ambition we claim to have?
The Pune hiring data suggests we are not. I hope other leaders are asking the same question — and acting on the answer.
Frequently asked questions
Is there a tech talent shortage in Pune?
There is a specific shortage of deep technical builders — hands-on DevOps and Data Engineering leaders, platform engineers. Coordination roles are oversupplied. The imbalance means organisations have abundant process management capacity but insufficient engineering depth to execute AI and platform programmes at the pace and quality required.
Why are there so many Scrum Masters but not enough engineers?
Coordination certifications are faster to acquire and historically well-compensated relative to the effort involved. Deep technical skills require years of hands-on work that many engineers bypass in favour of faster credential acquisition and management track progression. The talent market reflects a decade of those incentive structures playing out at scale.
What skills are most in demand for AI deployment in banking?
Data engineering depth, platform engineering, MLOps, and domain knowledge of banking regulations and data governance. These are builder skills — architecting pipelines, productionising models, securing inference infrastructure, building audit trails for regulated outputs. Severely undersupplied in the current market.
What is the difference between a coordinator and a builder in technology?
A coordinator manages the delivery process. A builder creates the technology. Both are necessary — but the ratio matters. When coordination capacity vastly exceeds builder capacity, you have the process infrastructure to deliver things you do not have the engineering depth to build. That gap becomes critical at the AI productionisation stage.
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Raj Thilak is Head of Technology for Data & Analytics with 24 years at Citi and Standard Chartered. He has managed teams of up to 450 professionals across global delivery programmes and specialises in data engineering, platform architecture, and fintech automation. Based in Pune, India. rajthilak.dev
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