AI Engineer
Also posted as: AI/ML Engineer · Applied AI Engineer · AI Software Engineer · AI Developer
An AI Engineer in India builds and ships the software that puts models into production: Python services that call LLM APIs, RAG pipelines over company documents, increasingly agentic workflows, and the deployment, monitoring and evaluation around them. The hiring is led by IT-services and GCC employers — Accenture, Infosys, TCS, HCLTech, Cognizant, plus captive centres like Convatec, Red Hat and Deutsche Bank — with Hyderabad and Bengaluru taking 16 of the 22 job ads behind this page. It is the broadest of the AI job titles (many are advertised as 'AI/ML Engineer'), and it skews senior: 11 of 22 job ads ask for 5+ years and only 3 are open to freshers at 4–9 LPA, so a working backend or data engineer switching in has the shortest path.
Capabilities employers ask for
How often employers ask for each capability, measured across the job descriptions behind this page. Click one to see what "knowing it" means, how to learn it, and how to prove it.
Integrate LLM APIs into an application
20 of 22 job ads are about wiring an LLM into an application — Deqode names OpenAI, Azure OpenAI, Claude, Gemini and Llama in one line, Red Hat wants Python apps that integrate LLMs. If you cannot call a model API with retries, streaming and token accounting, nothing else on this list matters.
Write production-quality Python for AI work
14 of 22 job ads name Python outright, and two of them make it the headline (Zimperium hires a 'Senior AI Engineer/Python Developer'; Accenture calls advanced Python with OOP mandatory). Only Talent500 accepts C# instead.
Build a grounded RAG application with citations
13 of 22 job ads require RAG, including both fresher listings — and the senior ones want it production-grade: Accenture asks for GraphRAG, Meraki Labs wants dual-corpus retrieval with provenance and rerankers, not a notebook demo.
Build a multi-step agent workflow
12 of 22 job ads ask for agents or agentic orchestration, naming LangGraph, CrewAI, AutoGen or Semantic Kernel; 'Agentic AI Engineer' is now the actual job title at Accenture, Infosys, Deqode and Synthlane.
Build and consume REST APIs
11 of 22 job ads expect you to ship the AI feature as a backend service — FastAPI/Flask/Django or Node microservices with auth and error handling. Infosys and DigitalXNode list 'scalable backend services and APIs using Python' as a core duty, not a bonus.
Deploy an AI service to the cloud
11 of 22 job ads require deploying on a cloud — Azure leads with 9 mentions, ahead of AWS and GCP at 7 each. Talent500 wants you to own Azure infrastructure and CI/CD pipelines; Docker appears in 5 job ads, Kubernetes in only 3, so learn Docker plus one cloud deploy first.
Generate embeddings and run vector search
9 of 22 job ads name embeddings or a specific vector store (Pinecone, FAISS, Weaviate, Chroma, Milvus, Azure AI Search). Meraki Labs goes further and asks for rerankers plus evidence of how you measured retrieval quality — that detail is what separates candidates here.
Design and version prompts systematically
8 of 22 job ads list prompt engineering, and Talent500 wants you to 'own prompt and system prompt architecture' and run structured experiments across prompt strategies. Show versioned prompts with a regression check, not screenshots of a chat.
Train and evaluate classical ML models
8 of 22 job ads still expect classical ML — scikit-learn, pandas, ML pipelines, model metrics — because half these jobs are advertised as 'AI/ML Engineer'. This is the main thing that separates this role from a pure GenAI role; know the sklearn workflow and how to read precision/recall.
Build an LLM evaluation harness
6 of 22 job ads ask you to measure model quality, and they are the most desirable employers in the set: Talent500 wants scoring rubrics and automated test suites, 100ms wants systematic LLM evaluations to catch regressions, Meraki Labs asks you to prove how you improved a metric.
Trace, monitor and debug LLM apps in production
6 of 22 job ads want AI apps monitored and debugged in production — Infosys pairs monitoring with governance, Convatec asks for DevOps/MLOps/AIOps observability, Talent500 says 'monitor, debug, and improve AI applications in production'. Tracing plus a cost dashboard is the cheapest way to look production-ready.
Implement tool / function calling
6 of 22 job ads describe the agent's hands rather than its brain: Accenture wants 'agents capable of tool use' and debugging of tool execution, Talent500 wants you to own tool/function definitions, and even the fresher listing at anvenstudios asks for API integrations and tool-based automation.
Apply guardrails, safety and privacy controls
Only 4 of 22 job ads name guardrails today — TCS wants security, governance and compliance across all implementations, Convatec adds GDPR and IAM, Deqode asks for hallucination reduction — but every one of them is an enterprise or GCC buyer, and India's DPDP rules push this into more JDs each quarter.
Expose and consume tools via MCP
Just 2 of 22 job ads name MCP and A2A — but they are Accenture's Lead Agentic AI Engineer and the Talent500 product role, i.e. the two most agent-mature JDs in the set. Building one MCP server is a weekend of work and it is the clearest signal that you follow where agent tooling is going.
Families: LLM application development · Programming foundations · Retrieval & knowledge systems · Agents & workflows · Cloud, deployment & production · Machine learning & data science · Evaluation, safety & observability
"AWS" on a JD is not "learn AWS"
The words employers write, translated into what they want you to be able to do for this role.
Skip, for now
- Kubernetes — Named in 3 of 22 job ads (Talent500, Convatec, Zimperium) and always next to a platform/DevOps list for 4+ year engineers. Docker appears in 5 and covers the interview question; learn K8s after you have one service deployed.
- Fine-tuning and training models from scratch — Not a single job ad in this set asks you to fine-tune an LLM. These employers want you to use hosted models well — retrieval, prompts, evals — and buy the model. Skip LoRA until a job actually names it.
- Deep learning and computer vision — Deep learning shows up in 3 job ads and computer vision in 2 (Accenture image processing, HCLTech), both inside long 'nice to have' lists. Know what a transformer does; do not spend a month on CNNs for this role.
- Knowledge graphs / GraphRAG — Mentioned by 2 of 22 (DigitalXNode, Accenture) and only as one option among many retrieval strategies. Get plain vector plus hybrid search working and measured first.
- Microsoft low-code stack (Copilot Studio, Power Platform, RPA) — Appears in 2 of 22 (Convatec, Talent500), both Microsoft-first shops. It is a fast learn once hired and near-useless everywhere else; do not lead your portfolio with it.
Enterprise document assistant: grounded RAG plus a tool-using agent, deployed and measured
Build a FastAPI service that ingests a real document set (policy PDFs, product manuals, or a public dataset), answers questions with cited RAG over a vector store, and escalates multi-step requests to a LangGraph agent that can call two or three tools — a database lookup, a ticket creation stub, and a human-approval step. Add an evaluation set of 40–50 questions with retrieval hit-rate and an LLM-as-judge score, trace every request, and deploy it with Docker to Azure Container Apps, Cloud Run or ECS. This is close to a line-by-line rebuild of the Infosys, Accenture and Talent500 JDs, which is exactly why it interviews well. Add one small scikit-learn component — for example a classifier that routes a query to the right corpus — because half these jobs are advertised as AI/ML Engineer.
- Every answer cites the source chunk and the app refuses or asks a clarifying question when retrieval confidence is low
- The agent completes a multi-step task using at least two tools with schema-validated JSON outputs and a human approval gate before any write
- An eval script runs in GitHub Actions and reports retrieval hit-rate and judge score; the README shows one change (chunk size, reranker, or prompt version) and what it moved
- Deployed on a public URL with request tracing, token and cost per request measured, and a short architecture note covering latency and failure modes
What the interviews look like
The rounds you'll actually face, in the order they usually come.
- 1
Screening
Recruiter or hiring manager confirms years of Python/backend experience, which cloud you have shipped on (Azure is asked most often here), which LLM and agent frameworks you have used in production, notice period and salary band. Have a deployed link and a one-line description of what it does for a business.
- 2
Technical / take-home
Live Python coding or a 2–3 day task: build a small RAG or agent over a supplied dataset, usually exposed as an API. Expect follow-ups on chunking strategy, embedding choice, why a specific answer was wrong, and how you would test it. IT-services loops also ask classical ML and OOP questions.
- 3
System/product design
Design a production AI system end to end: retrieval strategy, agent orchestration and tool boundaries, evaluation, monitoring, guardrails, cost and latency at scale. Senior loops (11 of 22 job ads want 5+ years) probe failure modes, rollback, and when not to use an agent at all.
- 4
Culture / stakeholder
Conversation with an engineering or delivery lead on working with cross-functional and client teams in Agile, explaining hallucination risk to non-technical stakeholders, and translating a vague business requirement into a solution design. GCCs and IT services also check domain comfort (BFSI, healthcare, manufacturing).
What people ask before choosing this role
Can a fresher get an AI Engineer job in India?
Yes, this is one of the more reachable AI-era roles. 3 of the 22 job ads behind this page accept 0–2 years of experience. The rest want more, so expect the fresher-friendly openings to be competitive.
What is the salary of an AI Engineer in India?
Entry-level roles cluster around ₹6–9 LPA, rising to about ₹60 LPA with experience. Every band on this page is quoted from a named source with a link, and pay varies widely by city, company type and whether the employer is an IT-services firm, a global capability centre or a product startup.
How long does it take to become an AI Engineer?
The six capabilities employers ask for most add up to roughly 114 focused hours — about 15 weeks at 8 hours a week, if you are starting from zero on all of them. Most people are not: the self-check on this page works out what you can skip, which is usually a large part of it.
What skills do you need for an AI Engineer role?
Across the 22 job ads behind this page, the most-requested capabilities are Integrate LLM APIs into an application (91% of ads), Write production-quality Python for AI work (64% of ads) and Build a grounded RAG application with citations (59% of ads). Note these are capabilities, not tools — employers write tool names, but what they are buying is the ability to do the work.
Which cities in India have the most AI Engineer openings?
Bengaluru (8), Hyderabad (8), Pune (2) and Delhi NCR (2) — counted across the 22 job ads behind this page. Remote-India roles are counted separately where the ad said so.
Is demand for AI Engineer roles in India growing?
Demand is growing faster than the supply of people who can do the work: Naukri JobSpeak put AI/ML roles at +33% YoY in July 2026 (+25% in June, against +6% for white-collar hiring overall), and Indian AI hiring rose 16% in June 2026 even as overall IT hiring fell. The Masai 2026 report projects 3.82 lakh AI job ads in India this year (+32%), with AI engineer roles specifically up 67% YoY, against a NASSCOM estimate of ~1 million AI-skilled professionals needed by 2027 versus a 5–6.5 lakh pool today.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 22 job ads behind it asked for one by name. What they ask for is evidence you can do the work — a public repo, a deployed project, something a hiring manager can open. That is what the path on this page is built to produce.
Companies with this role open in India
A sample of employers we saw hiring for this role — IT services, global capability centres, product companies and startups.
What it pays
How much demand
- 5,000'Artificial Intelligence Engineer' jobs in India (299 posted in the last day)linkedin
- 3,000'AI Engineer' jobs in India (206 posted in the last day)linkedin
- 53,710AI/ML vacancies in India, August 2026 (broad keyword match)naukri
- 714AI Engineer roles in India — the startup slice of the same marketwellfound
Where this role is heading
- Demand is growing faster than the supply of people who can do the work: Naukri JobSpeak put AI/ML roles at +33% YoY in July 2026 (+25% in June, against +6% for white-collar hiring overall), and Indian AI hiring rose 16% in June 2026 even as overall IT hiring fell.
- The Masai 2026 report projects 3.82 lakh AI job ads in India this year (+32%), with AI engineer roles specifically up 67% YoY, against a NASSCOM estimate of ~1 million AI-skilled professionals needed by 2027 versus a 5–6.5 lakh pool today.
Capability percentages come from 22 job descriptions read in full on 24-08-2026. How we do this