The rise of an FDE signals a new era for engineering talent

In India, 70% of the engineering graduates are considered employable, at least on paper. Ask a hiring manager the worth of that number, and you will get a very different perspective.

I have lost count of how many times a chief technology officer (CTOs) told me some version of this: “I don't need more engineers, I need the few who are wired for what comes next.” I have seen this first-hand.

A team of B.Tech engineers at a large enterprise client spent months building the technical pieces of an AI pilot, the tech worked, but it never reached production. Then, one engineer spent a week understanding what the business team actually needed and shipped a working version before the next sprint. Same degree. Same training. The gap was not technical skill, but the approach.


There is a new name for this breed of engineers – Forward Deployed Engineer (FDE). An FDE is embedded with a client to build the system inside their environment. The model, data pipeline, evaluation layer and the guardrails stays only until the client's own team can run it without them. A good FDE is designed to make their own presence unnecessary inside the organisation.

This is not an obituary for engineering or the B.Tech degree. Instead, it is about what has stopped being sufficient. Engineering has expanded before – civil and mechanical made room for computer science, and computer science made room for software. AI is opening a similar chapter, which can be understood across three phases.

The first was about possibility; what AI could theoretically do. Second, was about models: who could build and fine-tune them. The third is about deployment: who can make AI work inside a real business, with data and constraints.

So, we need to relook at the old employability model, which acts as a score, a placement percentage, a certification, or a resume line but not what one could ship. A strong engineer unpacks the problem, understands the constraints, and works out where technology can help. Sometimes that can be AI.

In addition, AI is quietly removing the entry rung of the career ladder. However, this does not equate with removing engineers but changing the nature of their hiring. Stanford's Digital Economy Lab found employment for software developers aged 22 to 25 fell nearly 20% from its late-2022 peak. This reflects AI can handle a portion of the work traditionally assigned to fresh graduates. But as AI moves from experimentation into deployment, the demand is shifting toward engineers who can take ownership of problems and turn technology into measurable outcomes.

MIT's research on enterprise GenAI adoption found that roughly 95% of pilots deliver no measurable business impact and traced the failure to missing integration, evaluation and security work. This is the work an undifferentiated B.Tech hire was never trained to do. So, this is likely to create more engineering work.

This is also where a common assumption breaks down. Many engineers assume becoming an FDE is a natural next step on their corporate ladder, but it is not the same. An FDE has to take deliberate training in a real-world application and not just model-building. But, most engineers have never had access to it.

Compared with a traditional systems or product engineer, an FDE carries more ambiguity, ownership, and client-facing judgement. The job is to work alongside the client, understand what needs to change, and move the needle on a real business problem. That shift, from technical execution to outcome creation, is what makes the FDE role so important.

The market has already placed its bet. Palantir invented the role more than two decades ago; OpenAI, Anthropic and Google are building dedicated FDE functions, and AWS and Microsoft have each committed over a billion dollars to embedding engineers directly into enterprise AI deployments.

Postings for the role jumped more than 700% year-on-year, and demand is so far outrunning supply Also, FDE compensation at frontier AI labs is now benchmarked against top research talent.

This has an implication beyond hiring, too. It changes what parents and students should look for in an engineering education as it is no longer enough for a course to explain how a neural network works. What matters is whether a student has used one to solve something real. A different question needs to be asked when evaluating a programme – does it put a student in front of a live, unresolved problem with a client before graduation? Or does it stop at simulated assignments and closed-book exams?

For academia, this is not a call to bolt an “AI module” onto an unchanged curriculum. It must change how students are trained. Real-world problem-solving cannot be taught through lectures and problem sets alone. We need real scenarios: ambiguous problems, incomplete data, a client who changes their mind halfway through, and the judgment calls that follow.

The old measure checked for marks, ranks, and a college's name on a resume. What should replace it is proof of application – a portfolio of shipped work with real stakes.

What is ending is not the engineer, but the assumption that a degree alone settles the question of who is ready for work. We are entering a deep-tech decade, and the engineers best positioned to thrive will be those who can take knowledge off the page and turn it into something that works in the real world.

Amar Srivastava is the CEO of Scaler's online business and Group Chief Product Officer. The views expressed in this article are personal.