What does a AI Engineer do?
An AI engineer ships products built on foundation models rather than training them — retrieval pipelines, agents and assistants wired into real systems, with evaluation harnesses that decide whether a prompt change actually helped. Indian teams have added this role quickly across SaaS and services, and the daily craft is retrieval quality, guardrails and holding latency and token cost inside budget.
A ai engineer usually has around 2–9 yrs of experience and earns typically ₹12L–₹45L/yr in India. The day-to-day blends Python, LLM APIs, RAG pipelines and more — this page gives you a ready-to-use ai engineer job description template you can copy, plus the exact skills and salary employers expect.
AI Engineer job description template
Copy the 9 responsibilities, 6 requirements and 10 skills below into your job post, then edit the parts that are specific to your company — pay band, location and reporting line.
Select the text above to copy it manually if the button is unavailable.
What are a AI Engineer's key responsibilities?
A ai engineer is typically responsible for the 9 duties below, which cover the day-to-day work most employers expect the role to own outright. Paste them into your job post as they are, or cut the ones another team already handles — a responsibility list that claims work the hire will not actually do is the fastest way to lose a candidate at offer stage:
- Build retrieval-augmented generation pipelines: chunking, embedding, reranking and source citation
- Choose and operate a vector store, tuning index parameters against measured recall
- Write evaluation sets and automated scoring so prompt and model changes are compared, not guessed
- Design agent and tool-calling flows with retries, timeouts and deterministic fallbacks
- Add guardrails against prompt injection, PII leakage and unsafe or off-topic output
- Serve models behind streaming APIs and manage caching, batching and context-window budgets
- Track token spend and p95 latency per feature and tune the model-size-versus-quality trade-off
- Fine-tune or adapt smaller models where prompting alone stops improving results
- Work with domain experts to turn messy internal documents into a usable knowledge source
What qualifications does a AI Engineer need?
Employers hiring a ai engineer in India usually ask for the 6 qualifications below, typically alongside 2–9 yrs of experience. Keep only the ones you will genuinely screen on: every extra must-have narrows the pool, and in the Indian market a long mandatory list filters out strong candidates whose background simply reads differently on paper:
- Strong Python plus real backend engineering — APIs, queues, caching and error handling
- Hands-on experience with at least one model provider API and an orchestration framework
- Understanding of embeddings, similarity search, chunking strategy and reranking
- Ability to build offline evaluations instead of judging output by eyeballing samples
- Awareness of prompt injection, data governance and what must never enter a prompt
- Comfort with cost and latency engineering under production traffic
What skills should a AI Engineer have?
These are the 10 skills employers name most often on live ai engineer listings in India. Treat the first few as the ones worth screening for directly and the rest as signals a candidate can pick up on the job — asking for all of them at once is what turns a reasonable role into an unfillable one:
Listing the three or four skills you genuinely screen on — rather than all 10 — is what keeps a ai engineer posting from filtering out candidates who could do the job.
What does a AI Engineer earn in India?
Typical salary (India)
typically ₹12L–₹45L/yr
Experience range
2–9 yrs
These are typical ranges and vary by city, company and skills. For live, role-specific pay data, see the OnJob salary guide.
AI Engineer job description — FAQs
How does an AI engineer differ from a machine learning engineer?
Machine learning engineers train, tune and deploy models built from an organisation's own data, living in feature pipelines and training runs. The applied variant usually starts from a pre-trained foundation model and spends its effort on retrieval, prompting, tool orchestration, evaluation and cost. Data scientists sit further upstream, answering questions with analysis and experiments rather than shipping a service.
What makes a retrieval system work well?
Retrieval quality decides almost everything. Good systems chunk documents along semantic boundaries, keep metadata for filtering, rerank candidates before they reach the model, and cite the passages used so answers can be checked. Weak systems dump fixed-size chunks into a vector index and blame the model when the answer comes back confidently wrong.
How do you evaluate an LLM feature before shipping it?
Evaluation starts with a fixed set of representative inputs and expected qualities, scored through a mix of exact checks, retrieval metrics and model-graded rubrics with human spot-checks. Running that suite on every prompt or model change turns tuning into measurement. Production telemetry — ratings, escalations, abandonment — then feeds fresh cases back into the set.
Do you need a research background for these jobs?
Research credentials help for model training work but rarely gate application roles, which reward strong software engineering, systems thinking and evaluation discipline. Indian postings usually ask for Python depth, backend experience and shipped projects. A public demo with honest evaluation numbers often carries more weight in an interview than coursework does.