Hiring an AI engineer in India? Do not fit buzzwords in your job description.
Here’s how to get it right.
- Know what you’re actually hiring for: “AI engineer” is not one role. Someone integrating LLM APIs into your product is different from someone building RAG pipelines, which is different from someone training models, which is different from a data scientist running experiments. If you write “AI engineer” and mean “person who wires GPT into our app,” a model-training specialist will apply and neither of you will get what you want.
Describe the work: “integrate LLM APIs into our product,” or “build retrieval pipelines over our data,” or “train and fine-tune models.” The words you use decide who applies. - Separate demos from production: This is the single most important line in an AI engineer JD. Plenty of people can build an impressive demo in a notebook. Far fewer can ship an AI feature that survives real users, handles edge cases, manages cost, and doesn’t fall over at 2am. Say it plainly: “You’ve shipped AI to real users and you know the difference between a demo and a production system.” That one sentence filters hard, in a good way.
The words decide the applicants. A quick look:
| What you write | Who it attracts |
| “Work on cutting-edge AI” | People chasing hype, and demo-builders |
| “Ship AI features to real users” | Engineers who’ve run things in production |
| “AI engineer” (no detail) | A mix of everyone, most of them wrong |
| “Integrate LLM APIs into our product” | People who do exactly that job |
| “Experience with AI” | Notebook tinkerers |
| “Debugged a broken AI feature in production” | People who’ve survived the hard part |
- Name the real responsibilities: Not “work on AI.” The actual work. Integrate and tune LLM APIs. Build retrieval pipelines and prompt systems. Write evaluation frameworks to measure accuracy, latency, and cost. Monitor features in production and fix what breaks. When an AI engineer reads specifics like these, they know you understand the job. When they read “work on cutting-edge AI,” they know you don’t.
- Don’t forget how they work: At an early startup, the AI engineer talks to founders daily, makes calls with incomplete information, and treats the product like it’s theirs. These traits matter as much as the tech. Name them.
- Get the stack right, and split it: What’s required day one versus nice to have. Splitting it stops you from accidentally filtering out great people over a “nice to have.”
| Required day one | Nice to have |
| LLM APIs (OpenAI, Anthropic, or equivalent) | LangChain, LlamaIndex, or LangGraph |
| RAG pipelines and vector databases | Fine-tuning or open-source models |
| Python for AI and data work | A specific data background (e.g. IoT input) |
| Prompt engineering and evaluation frameworks |
- Benchmark the pay. AI engineers are one of the scarcer talent pools, and the good ones know their worth. In the Indian product market, an AI/ML engineer with 3 to 5 years sits around 25 to 35 LPA, and 5 to 8 years around 35 to 45 LPA. Guess low and you won’t hear back.
Now the honest part. That’s a lot to get right, and the AI hiring market shifts every few months. This is exactly what the AI Job Description Builder handles for you. You say you need an AI engineer and why. It picks the right sub-label, writes the production-versus-demo language, sets the responsibilities, splits the stack, benchmarks the salary, and adds 30, 60, 90 day milestones built for an AI role.
You bring the problem. It writes the JD that gets you the right applicants. It’s built for hiring India-based engineering talent, with AI/ML salaries benchmarked to the Indian product market.
