AI Engineer
Also posted as: Artificial Intelligence Engineer · AI/ML Engineer · Applied AI Engineer · AI Software Engineer · AI Developer · Generative AI Engineer · LLM Engineer · GenAI Developer · AI Application Developer · Agentic AI Engineer
An AI Engineer in India ships the software that puts models into production: Python services that call LLM APIs, RAG pipelines over company documents, agent workflows that plan and call tools, and the deployment, evaluation and monitoring around all of it. Hiring is led by IT services and GCCs — Accenture, Infosys, TCS, Wipro, Cognizant, HCLTech, Bosch — alongside product startups, and Bengaluru plus Hyderabad account for 31 of the 54 job postings behind this page. The same job is advertised as AI/ML Engineer, Generative AI Engineer or Agentic AI Engineer, which is why this page tracks them as one role; it skews senior — 23 of 54 postings ask for 5+ years and only 4 are open to freshers — so a working backend, data or ML engineer switching in has the shortest path.
- Across 161 AI Engineer job postings in India, the most-requested capabilities are Write production-quality Python for AI work (78%), Build a grounded RAG application with citations (75%) and Build a multi-step agent workflow (73%).
- Pay at 5+ yrs averages about ₹21.1 LPA (verified across 2 salary sites: AmbitionBox, Glassdoor); employers offer ₹30–50 LPA (median of 17 job postings that state pay, 5+ yrs · Wellfound, LinkedIn, Cutshort, Naukri).
- 33,000+ open roles in India — verified across 2 portals (naukri, glassdoor), checked 04-10-2026.
- Hiring is concentrated in Bengaluru, Hyderabad and Delhi NCR.
- Postings read from LinkedIn 41%, company career pages 24%, Wellfound 21%, other portals 7%, Naukri 4% and cutshort 2%.
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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.
Have a posting open? Check it against this map →
Write production-quality Python for AI work
Python is the default language of this role, and employers want it production-grade, not notebook-grade. Zimperium hires a 'Senior AI Engineer/Python Developer' on Django, Flask or FastAPI, Lynx Analytics wants 'clean, well-tested Python code' and code reviews, and EXT India asks for 'Async Python, FastAPI or equivalent, production-grade backend code'; Talent500 accepts C# and Cognizant Java, so it is the main door rather than the only one. Write typed, tested, packaged Python that someone else can run and maintain.
Explore 4 practice tools →Build a grounded RAG application with citations
RAG is the standard pattern here, and the better postings want it working in production rather than in a demo notebook. Bosch spells out 'chunking, embeddings, and vector' retrieval, EXT India wants 'RAG systems that actually work, hybrid retrieval, re-ranking, context management', Meraki Labs asks for production RAG, search or IR experience, and even anvenstudios' fresher role builds 'RAG-based internal knowledge assistants'. Build a retrieval app that answers from your own documents, cites its sources and has a measured answer quality you can show.
Explore 5 practice tools →Build a multi-step agent workflow
Agents are now a large part of the job, and 'Agentic AI Engineer' is a real title at TCS, Capgemini and Synthlane. Cloudmetica wants multi-agent LangGraph workflows 'focusing on cycles, persistence, and complex state management', TCS names LangChain, AutoGen and CrewAI, and Convatec builds agents in Copilot Studio and Foundry, while Lynx Analytics hires juniors to maintain agentic pipelines under senior guidance. You must be able to build an agent that plans, calls tools, keeps state across steps and recovers when a step fails.
Explore 4 practice tools →Build and consume REST APIs
The AI feature ships as a backend service, so employers expect ordinary API engineering. DigitalXNode asks you to 'Build APIs using Python and FastAPI', Barclays wants API development in FastAPI, Dscout lists 'backend services, APIs, data models, and feedback pipelines', and Deutsche Bank's role is Java first; Cloudmetica even exposes internal APIs as tools for agents. You must be able to wrap a model behind an authenticated REST endpoint with validation and sensible errors, and consume other teams' APIs just as comfortably.
Explore 3 practice tools →Generate embeddings and run vector search
Retrieval quality is its own skill, and employers name the parts. MyRemoteTeam wants you to 'design and implement Vector Database solutions for semantic search', Pulsora asks for 'embeddings, vector databases, and advanced rerankers', Redica Systems names Pinecone, Neo4J and hybrid search, and Thomson Reuters wants knowledge of vector databases and embeddings. Learn to pick a chunking strategy and embedding model, run vector and hybrid search, add a reranker and measure whether the right passages come back.
Explore 4 practice tools →Deploy an AI service to the cloud
Employers expect you to put the service on a cloud yourself, and they split across all three. Accenture's AI/ML Engineer wants ML pipelines in the cloud with GCP preferred, OrangePeople wants AWS and Amazon Bedrock, Optum asks for Azure OpenAI and Azure AI Services, and SciSpace wants open-source LLMs deployed 'using containerization tools like Docker'. Learn to containerise an AI service and deploy it to one cloud with secrets, IAM and logging set up properly, then the other two are mostly vocabulary.
Explore 4 practice tools →Design and version prompts systematically
Prompt engineering appears constantly, and the stronger postings treat it as engineering rather than typing. Dscout wants 'prompting and context engineering as an engineering discipline', Vyn pairs prompt engineering with 'context design, and guardrail development', and TCS wants prompts optimised together with retrieval; Ethical Den warns it is 'not looking for someone whose experience is limited to prompt engineering'. Keep prompts versioned with a regression check so you can prove a change helped, and show that alongside real engineering work.
Explore 4 practice tools →Integrate LLM APIs into an application
This is the floor of the job: wiring a hosted model into real software. Deqode lists 'LLM APIs (OpenAI/Anthropic/Gemini)' alongside Azure OpenAI, Convatec builds on Azure AI Foundry, Copilot Studio and Azure OpenAI, Red Hat wants 'Python-based applications integrating LLMs', and Persistent Systems asks for 'OpenAI-compatible APIs, Chat Completions, function/tool calling'. You need to call a model API from code with retries, streaming, timeouts and token accounting, and switch providers without rewriting the app.
Explore 4 practice tools →Train and evaluate classical ML models
This title has not fully separated from the older AI/ML Engineer, so classical ML still shows up. Grab wants 'Command of fundamental ML concepts (Bagging, Boosting, Online Learning)', Accenture's AI/ML Engineer is about automating ML pipelines, Trexquant asks for deploying machine learning models in production, and Staffnixcom advertises a 'Data Scientist / AI Engineer'. Know the scikit-learn workflow and how to read precision, recall and a confusion matrix so these interviews do not surprise you, but do not start your learning here.
Explore 5 practice tools →Build an LLM evaluation harness
Measuring quality is what separates people who have shipped from people who have demoed. Talent500 wants 'evaluation frameworks for agent output quality — scoring rubrics, automated test suites', Shuru asks you to 'Build evals to measure accuracy and prevent regressions', Interactly.ai wants evals treated 'as a first-class concern not an afterthought', and PwC wants frameworks for testing agent performance and safety. Build a small labelled eval set and a scorer that runs on every prompt or model change, and bring the results to interviews.
Explore 4 practice tools →Implement tool / function calling
This is the mechanism under the agentic buzzword, and the specific postings ask for it by name. Pocketpills wants 'tool calling, function calling, structured outputs, and context/memory management', SaralX wants function calling against banking APIs, Cloudmetica grades tool-selection accuracy in production, and GoComet wants you to debug 'tool misuse' and 'agent loops'. Define clean tool schemas, validate the arguments the model produces, handle tool errors and retries, and log which tool was chosen and why.
Explore 4 practice tools →Apply guardrails, safety and privacy controls
Guardrails show up wherever the output touches money, health or customer data. UST wants guardrails for 'application safety, compliance, responsible AI usage, and security', Luminova lists 'prompt-injection defence, content filtering, PII redaction, and hallucination checks', SaralX wants 'confirmation flows for money movement' and PII redaction against banking APIs, and Mindtickle wants guardrails to keep hallucinations out of critical business workflows. Be able to validate inputs and outputs, redact personal data, test for prompt injection and fail safely when the model is wrong.
Explore 3 practice tools →Expose and consume tools via MCP
The Model Context Protocol has moved from nice-to-have into real job duties. MongoDB's AI teams own the MongoDB MCP Server, Pure Storage wants 'high-performance Model Context Protocol (MCP) servers', Sagent implements MCP tool servers with FastMCP and Streamable HTTP, and Transnational AI titles a fresher role 'Agentic AI Engineer (MCP)'. Build and ship one MCP server that exposes a real tool, then consume it from an agent; it is a small project and a clear signal you follow where agent tooling is going.
Explore 3 practice tools →Trace, monitor and debug LLM apps in production
Employers want to see inside the system once it is live. Databricks asks for 'comprehensive observability and tracing for agent runs', Grab and Pocketpills name LangSmith, Novartis lists Langfuse, and eBay wants monitoring, tracing and alerting plus incident response and postmortems. Instrument every model and tool call with inputs, outputs, tokens, latency and cost, and practise finding the failing step in a trace.
Explore 4 practice tools →Families: Programming foundations · Retrieval & knowledge systems · Agents & workflows · Cloud, deployment & production · LLM application development · 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 4 of 54 job postings (Talent500, Convatec, Zimperium, Wipro) and always inside a platform/DevOps list for 4+ year engineers. Docker appears in 12 and covers the interview question; learn K8s after you have one service deployed.
- Fine-tuning and training models from scratch — Only 3 of 54 job postings mention fine-tuning at all (Wipro as a workflow, Cognizant's GenAI role, Trexquant's LLM Engineer), and only Trexquant makes it the job. These employers want you to use hosted models well — retrieval, prompts, tools, evals — and buy the model. Skip LoRA until a JD actually names it.
- Deep learning and computer vision — Deep learning appears in 8 of 54 job postings and computer vision in 3 (Accenture image processing, HCLTech, Accenture's LLM full-stack ad), all 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 6 of 54 (Accenture, DigitalXNode, Bosch, Lynx Analytics, TCS, Credo AI) and still only as one option among several retrieval strategies. Get plain vector plus hybrid search working and measured first, then read up on GraphRAG.
- Microsoft low-code stack (Copilot Studio, Power Platform, RPA) — Appears in 5 of 54 (Convatec, Talent500, Bosch's Teams telephony role, TCS with UiPath, Cognizant with Microsoft Copilot), all 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, Cloudmetica 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 15 of the 54 job postings still ask for classical ML.
- 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, at 19 of 54 postings), 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, since 15 of 54 postings still carry ML requirements.
- 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 — 23 of 54 job postings 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. 23 of the 161 job postings 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?
Pay at 5+ yrs averages about ₹21.1 LPA (verified across 2 salary sites: AmbitionBox, Glassdoor); employers offer ₹30–50 LPA (median of 17 job postings that state pay, 5+ yrs · Wellfound, LinkedIn, Cutshort, Naukri). Not every posting states pay, and pay varies widely by city and by 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 112 focused hours — about 14 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 161 job postings behind this page, the most-requested capabilities are Write production-quality Python for AI work (78% of postings), Build a grounded RAG application with citations (75% of postings) and Build a multi-step agent workflow (73% of postings). 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 post the most AI Engineer jobs?
Bengaluru (55), Hyderabad (26), Delhi NCR (19) and Remote (India) (17) — counted across the 161 job postings behind this page. Remote-India roles are counted separately where the posting said so.
Is demand for AI Engineer roles in India growing?
Naukri JobSpeak put AI/ML hiring up 31% year on year in August 2026, the strongest segment against 14% for white-collar hiring overall, after +33% in July; a CIEL HR report (Sep 2026) found demand for agentic AI engineers up 260% on 2025. The demand is mostly senior: of the 46 AI Engineer job postings collected here with a posted date since 1 September, 29 ask for 5+ years and only 2 are open to 0-2 years.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 161 job postings 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. Each links to one of the company's postings for this role, checked open on 04-10-2026; where none is open, to its current openings instead.
What it pays
most earn ₹5.8–13.8 LPA (base pay) · verified across 2 salary sites: AmbitionBox, Glassdoor
Employers offer ₹4–8 LPA: median of 13 job postings that state pay, 0–2 yrs · other sites, Wellfound, Naukri
verified across 2 salary sites: AmbitionBox, Glassdoor
Employers offer ₹12–23 LPA: median of 25 job postings that state pay, 2–5 yrs · Wellfound, Naukri, LinkedIn, Cutshort
verified across 2 salary sites: AmbitionBox, Glassdoor
Employers offer ₹30–50 LPA: median of 17 job postings that state pay, 5+ yrs · Wellfound, LinkedIn, Cutshort, Naukri
Across all levels: the middle half earns ₹6.5–17.9 LPA · Glassdoor
How much demand
What each job portal shows for this role's title — the readings behind the openings figure above.
- 44,23795 of 96 inspected results carry the title · checked 04-10-2026naukri
- 22,31612 of 12 inspected results carry the title · checked 04-10-2026glassdoor
- at least 11,00029 of 35 inspected results carry the title · disagrees with the others, not used · checked 04-10-2026linkedin
- at least 1,000exact-phrase search · disagrees with the others, not used · checked 04-10-2026indeed
Where this role is heading
- Naukri JobSpeak put AI/ML hiring up 31% year on year in August 2026, the strongest segment against 14% for white-collar hiring overall, after +33% in July; a CIEL HR report (Sep 2026) found demand for agentic AI engineers up 260% on 2025.
- The demand is mostly senior: of the 46 AI Engineer job postings collected here with a posted date since 1 September, 29 ask for 5+ years and only 2 are open to 0-2 years.
Capability percentages come from 161 job descriptions read in full on 03-10-2026. How we do this