A career path from full-stack into AI engineering in India

A career path from full-stack into AI engineering in India

We run a full-stack internship in Sikar and we hire and staff AI work for clients. The people who get AI tickets are not the ones who watched the most model launch streams. They are the ones who can ship an API, then add retrieval without lying about quality.

What "AI engineer" means in the jobs we see

Most Indian product roles still want:

• A web or mobile surface people use.
• An API with auth.
• A model call that is grounded, logged, and cheap enough to leave on overnight.

That is closer to full-stack plus RAG than to a research lab. If you cannot deploy Next.js and Postgres, pause the LLM course.

A sequence that has worked for graduates

1. Keep shipping product. One public repo with auth, a database, and a README that runs.
2. Add a thin RAG slice on your own docs or a client's FAQs. Citations on every answer. A list of questions you test every week.
3. Measure. Latency, token cost per successful task, and a human pass/fail on 20 questions. Write the numbers in the README.
4. Learn one provider adapter. OpenAI today, something else tomorrow. See OpenAI vs Anthropic for production.
5. Do not fine-tune first. RAG vs fine-tuning is the decision we make on client work.

Ollama is useful for local experiments and privacy-sensitive demos. Production user traffic in India often still hits a hosted API because of GPU cost and ops. Be honest about which one you ran.

Interviews

Bring:

• A diagram of retrieve, assemble, generate, log.
• A failure: a hallucinated policy answer you caught with an eval.
• A cost note: what happened when you stuffed 40 chunks into the prompt.

Do not bring a slide that says you "mastered LangChain." Bring a repo a reviewer can clone.

What we do not promise

The internship is not a placement agency. Career support is resume, LinkedIn, and interviews that use your six projects. AI roles still expect you to debug a 500 from your own API at 11pm.

If you want to learn this stack with live review, see the program. If you want it built on a product, schedule a call.