The Applied AI Skills Gap in Indian Engineering Education: What Graduates Miss and How to Close It

DOI : 10.17577/

Indian engineering colleges produce graduates who understand AI theory well, but most enterprise AI work now starts after the model is trained: connecting it to messy client data, shipping it to production and keeping it running. That is the applied AI skills gap, and it is widening as companies move from pilots to live deployments.

A final-year student can usually explain backpropagation, build a classifier in a notebook and report accuracy on a clean dataset. The same student is often unsure what to do when the data sits in three legacy systems, the client changes a requirement twice in a week, or a model that worked in testing proves too slow and too costly in production. Programmes such as Scaler’s forward deployed engineer course were built around exactly this deployment end of the work. This article looks at where the gap shows up, what hiring data says about it, which skills curricula tend to miss and how students and institutions can close it.

What the gap looks like in practice

The gap is not about intelligence or effort. It comes from the difference between the problems a degree trains for and the problems a deployment team faces.

From notebook to production: where graduates get stuck

A typical enterprise AI project moves through six stages. Most degree projects cover only the third.

1. Understand the client’s process. Map how the work is done today, who does it and what a better outcome would look like.
2. Get and clean the data. Find where the data lives, agree access, and deal with gaps, duplicates and inconsistent formats.
3. Build a baseline model. Train and test a first version against a measurable target.
4. Wrap it in a service. Expose the model through an API with authentication, logging and error handling so other systems can use it.
5. Test with real users. Check outputs against the people who will rely on them, and fix what the test set did not reveal.
6. Monitor and hand over. Track quality and cost after launch, document the system and train the client’s team to run it.

Three job families touch these stages, and they ask different questions.

Graduates who have only seen stage three tend to be productive on model work but slow on the integration, reliability and communication work that decides whether an AI project survives its first quarter in production.

India’s AI talent gap in numbers

The best-known employability benchmark for Indian graduates is the Mercer | Mettl India Graduate Skill Index 2025, which analysed data from more than 2,700 campuses and over one million students. It found that 42.6% of graduates who apply for jobs are overall employable, and that 46.1% are job-ready for AI and machine learning roles. The report also points to growing employer demand for soft skills such as critical thinking, communication and learning agility.

Those figures suggest that technical AI knowledge is only part of what employers screen for. The skills that decide success on a client project, such as scoping a problem, explaining trade-offs and handing a system over, sit largely outside the classroom.

Employers are now hiring specifically for the deployment end of AI work. One title that captures this is the forward deployed engineer (FDE): an engineer who works with a client to build, deploy and maintain an AI system inside the client’s own environment.

• Indeed India: FDE job postings grew nearly 19-fold between June 2025 and June 2026, and were up 1,807% year on year in June 2026, according to Indeed data reported by PTI in October 2026.
• TeamLease Digital: the staffing firm reported demand for FDE-type roles rising about 800% in the first three quarters of 2025, with roughly 250 to 270 open FDE positions in India by mid-2026.
• Individual employers: Business Standard reported in July 2026 that Coforge plans to grow its FDE team from 20 to 100, and EPAM India from 50 to 100.

These numbers start from a small base, so they show direction more than market size. Still, listings now appear across Bengaluru, Hyderabad, Chennai, Delhi and Mumbai, as well as in remote roles. Taken together, they suggest employers see a shortage of people who can take an AI system from prototype to a client’s production environment.

Five skills curricula often leave out

1. Data integration. Pulling data from legacy databases, spreadsheets and APIs, cleaning it and building pipelines that keep running when a source changes.
2. Production backend work. Serving a model behind a reliable API, with queues, caching, authentication and sensible handling of failures.
3. Evaluation and observability. Measuring whether a retrieval or agent system gives correct answers, and tracing failures once real users are on it. Accuracy on a test set is only the start.
4. Requirement discovery. Asking the right questions of non-technical stakeholders, writing down what the client actually needs and agreeing what “done” means.
5. Communication and handover. Explaining trade-offs in plain language, documenting the system and training the client’s team to run it.

None of these is difficult to teach. They are simply hard to teach from slides, because each depends on working through a realistic, imperfect problem.

For a view of what degree programmes already cover, IJERT’s explainer on AI bachelor degree programmes sets out the skills they teach and the jobs they lead to. Its paper on AI in undergraduate computer education looks at AI-based tools for teaching programming concepts. Reading them alongside the list above shows where classroom coverage ends and project work has to begin.

A worked example: a support-ticket assistant

The following is an illustrative scenario, not a real client. A mid-sized company wants an AI assistant that sorts incoming customer support tickets and drafts replies.

The notebook version takes an export of past tickets, trains a classifier and reports strong accuracy on a held-out sample. It works on the data it was given.

The deployed version raises questions the notebook never faced:

• Tickets arrive from email, chat and a web form in different formats, so ingestion needs its own pipeline.
• The support team uses category names that differ from the labels in the training data, so requirements have to be agreed before anything is built.
• Replies must not expose customer data, so the system needs access controls and logging.
• Accuracy drifts as new products launch, so quality has to be measured on live tickets every week.
• When the assistant is wrong, an agent needs a clear way to correct it and feed that correction back.

None of these questions concerns model theory. All of them decide whether the support team keeps using the system after the first month.

Ways to close the gap

Students and early-career engineers have four realistic routes.

• Build one project end to end. Choose a messy real dataset, ingest it, serve a model behind an API, add monitoring and write a handover note. One finished system teaches more than five notebooks.
• Seek deployment-facing internships. Teams that work directly with customers expose students to requirements, constraints and production failures early.
• Use industry mentors. Reviews from engineers who ship AI systems catch the habits that coursework rewards but production punishes.
• Take structured, project-based training after graduation. Programmes of this kind give learners a realistic client engagement to work through. Scaler’s, for example, is a 12-month live online programme in which learners work through a single simulated client engagement, from legacy data ingestion to a production backend and a redesign for scale, and then present a capstone on a new client brief.

Whichever route a learner takes, the test is the same: can they show a working system and explain the decisions behind it?

What institutions can do

Colleges do not need to rebuild their programmes to narrow the gap. Three changes would help most:

1. Replace some capstone topics with external briefs from companies, so students work to a client’s requirements instead of their own.
2. Add a deployment lab where students serve, monitor and break their own models.
3. Invite practitioners to review projects, not only to give guest lectures.

IJERT’s article on AI in education: opportunities and risks covers the wider picture of how AI is entering teaching and administration, which is a useful backdrop for these changes.

The demand data above suggests that employers will keep paying for engineers who can take AI from a notebook into a client’s production environment. Closing the skills gap is a curriculum design problem, and it is a solvable one.

Frequently asked questions

Why do engineering graduates struggle to get AI jobs? Many have strong theory but little experience with messy data, production systems and client communication. Employers hiring for deployment work look for evidence of all three.

What is the difference between an ML engineer and a forward deployed engineer? An ML engineer focuses on training, serving and monitoring models reliably. A forward deployed engineer does similar technical work but also works directly with a client, scoping the problem and handing over the finished system.

Can a student build these skills without a job? Yes. Building one project end to end, on realistic data, with an API, monitoring and a written handover note, covers most of the gap. Reviews from working engineers make the learning faster.

Is the demand for FDE roles real or hype? The growth rates are large because they start from a small base. Postings and named hiring targets from firms such as Coforge and EPAM India suggest the demand is real, but still modest in absolute numbers.