GenAI / LLM Application Engineer
Also posted as: Generative AI Engineer · LLM Engineer · GenAI Developer · AI Application Developer · Agentic AI Engineer
A GenAI / LLM Application Engineer builds the software around large language models: wiring OpenAI, Anthropic or open-source models into APIs, RAG pipelines and increasingly multi-step agents, then deploying and monitoring them on AWS, GCP or Azure. In India the hiring is led by IT-services majors (Wipro, TCS, Cognizant, Accenture, HCLTech, Infosys) building client solutions, plus funded startups in Bengaluru, NCR and remote-first teams. The role exists because enterprises now want production-grade LLM features rather than demos, and it pays 25-40% above generalist ML engineering; most job postings want 2+ years of software experience, so it suits developers switching in more than fresh graduates.
- In the 25 GenAI / LLM Application Engineer job postings read in India before the title was retired, the most-requested capabilities were Integrate LLM APIs into an application (80%), Write production-quality Python for AI work (52%) and Build a multi-step agent workflow (52%).
- At least 6 open roles in India — the dated job postings for this role we read in the last 60 days; portals count a title's exact phrase, which undercounts a job posted under many titles, checked 20-08-2026.
- Hiring is concentrated in Bengaluru, Remote (India) and Delhi NCR.
- Postings read from company career pages 32%, LinkedIn 24%, Wellfound 20%, cutshort 12%, other portals 8% and Naukri 4%.
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 25 job postings are about wiring OpenAI/Anthropic/Gemini or open-source LLMs into a real application — this is the job, not a nice-to-have.
Explore 4 practice tools →Write production-quality Python for AI work
13 of 25 job postings name Python outright (FastAPI, Django, async Python); nearly every agent/RAG framework asked for is Python-first.
Explore 4 practice tools →Build a multi-step agent workflow
13 of 25 job postings ask for agentic or multi-agent workflows, naming LangGraph, CrewAI or AutoGen — 'Agentic AI Engineer' is now a title in its own right at TCS, Wipro and Cognizant.
Explore 4 practice tools →Build a grounded RAG application with citations
10 of 25 job postings require production RAG; EXT India and Pulsora specifically want hybrid retrieval and re-ranking, not a tutorial-grade pipeline.
Explore 5 practice tools →Build and consume REST APIs
10 of 25 job postings expect you to expose LLM features as REST services (FastAPI, Django, Node) — Cognizant wants microservices, Accenture and IntelliSavvy want full-stack.
Explore 3 practice tools →Deploy an AI service to the cloud
9 of 25 job postings require deploying on AWS, GCP or Azure; EXT India says it plainly: 'deploy what you build'.
Explore 4 practice tools →Generate embeddings and run vector search
8 of 25 job postings name embeddings or a vector DB (FAISS, Pinecone, Weaviate, Chroma, pgvector) — the retrieval layer under every RAG and agent-memory requirement.
Explore 4 practice tools →Design and version prompts systematically
8 of 25 job postings list prompt engineering explicitly, and Vyn wants 'context design' and consistent outputs — expect to show versioned prompts, not one-off chats.
Explore 4 practice tools →Implement tool / function calling
8 of 25 job postings want agents that call tools, APIs and databases (TCS: 'tool calling'; EXT India: 'function calling', structured outputs) — the mechanism that makes an agent useful.
Explore 4 practice tools →Build an LLM evaluation harness
5 of 25 job postings ask for LLM evaluation (Wipro: 'LLM output evaluation'; EXT India: 'LLM-as-judge') — under a quarter today but every serious agent job posting includes it, so build an eval harness early.
Explore 4 practice tools →Trace, monitor and debug LLM apps in production
5 of 25 job postings mention tracing, monitoring or debugging LLM apps in production (LangSmith, Helicone, Datadog) — growing alongside agent deployments.
Explore 4 practice tools →Apply guardrails, safety and privacy controls
4 of 25 job postings require guardrails or fallback mechanisms for safe AI behaviour — even the one fresher job posting (Transnational AI) asks for it.
Explore 3 practice tools →Manage context windows and memory
4 of 25 job postings ask for context optimisation or agent memory (short/long-term, vector or graph) — a differentiator once you move beyond single-turn RAG.
Explore 4 practice tools →Expose and consume tools via MCP
Only 3 of 25 job postings name MCP, but they are Wipro, TCS and a fresher-level role — the protocol is moving from startup toolkits into IT-services JDs, and the trend notes show agent-protocol skills growing 300%+.
Explore 3 practice tools →Families: LLM application development · Programming foundations · Agents & workflows · Retrieval & knowledge systems · Cloud, deployment & production · 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
- Fine-tuning / LoRA — Named in 3 of 25 job postings, and two of those are model-training roles (Cognizant Gen AI, Trexquant). Most jobs want you to use hosted or open models well, not train them.
- Deep learning / PyTorch from scratch — Only 4 of 25 job postings ask for PyTorch or TensorFlow, all at 5+ years or research-flavoured. Know what a transformer is; skip building one.
- Kubernetes — Named in 1 of 25 job postings (Wipro, alongside a long list). Docker plus one cloud deploy covers 9 of 25 job postings; learn K8s once you are on a platform team.
- Managed AI platforms (Bedrock / Vertex / Azure AI Foundry) — Only TCS and Accenture name them. Learn the model APIs directly first; the platform wrappers are a week of reading once you are hired.
- RPA tools (UiPath, Automation Anywhere) — Appears in 1 of 25 job postings (TCS enterprise integration). Not a GenAI engineering skill; ignore unless targeting that specific team.
Support-ticket agent with grounded answers and an eval harness
Build a FastAPI service that ingests a company's help-centre docs into a vector store, answers customer questions with cited RAG, and hands off to a LangGraph agent that can call tools (look up an order, create a ticket, escalate to a human). Add an evaluation set of 50 questions scored by an LLM-as-judge plus retrieval hit-rate, trace every request with LangSmith or an open-source equivalent, and deploy it with Docker to a free tier on GCP or AWS. This mirrors what Wipro, EXT India and Pulsora describe almost line for line.
- Answers cite the source chunk and refuse when retrieval confidence is low (guardrail path is tested)
- Agent completes a multi-step task using at least two tools with structured (JSON) outputs, with a human-approval step before any write action
- Eval harness runs in CI and reports retrieval hit-rate and judge score; a README shows one change that moved a metric
- Deployed public URL with tracing dashboard, cost per request measured and documented
What the interviews look like
The rounds you'll actually face, in the order they usually come.
- 1
Screening
Recruiter or hiring manager checks years of Python/backend experience, which LLM frameworks you have shipped (LangChain, LangGraph, CrewAI), cloud exposure and salary band. Have a deployed project link ready.
- 2
Technical / take-home
Live Python coding or a 2-3 day take-home: build a small RAG or agent over a given dataset, often with FastAPI. Expect questions on chunking, embeddings, prompt versioning and why an answer was wrong.
- 3
System/product design
Design a production LLM system: retrieval strategy, agent orchestration, tool calling, evals, observability, guardrails, cost and latency. Senior loops probe failure modes, human-in-the-loop and when not to use an agent.
- 4
Culture / stakeholder
Discussion with product or client-facing leads on explaining LLM trade-offs, handling hallucinations with users, and working in Agile with cross-functional teams; IT-services firms also check domain comfort (BFSI, healthcare, retail).
What people ask before choosing this role
Is GenAI / LLM Application Engineer still a role in 2026?
Not as a separate title — it has not been tracked since 04-09-2026. The advertised titles never separated: 'Generative AI Engineer' and 'Agentic AI Engineer' appeared under both searches, and a quarter of the postings collected here were titled plainly 'AI Engineer'. Tracking them apart split one market in two. Employers post this work as AI Engineer; that page is kept current.
Can a fresher get a GenAI / LLM Application Engineer job in India?
Realistically, no — not straight away. Only 1 of the 25 job postings behind this page accept 0–2 years; most want people who have already shipped software or run projects. It is a strong second move rather than a first job.
How long does it take to become a GenAI / LLM Application 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 a GenAI / LLM Application Engineer role?
Across the 25 job postings behind this page, the most-requested capabilities are Integrate LLM APIs into an application (80% of postings), Write production-quality Python for AI work (52% of postings) and Build a multi-step agent workflow (52% 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 GenAI jobs?
Bengaluru (9), Remote (India) (6), Delhi NCR (4) and Hyderabad (3) — counted across the 25 job postings behind this page. Remote-India roles are counted separately where the posting said so.
Is demand for GenAI roles in India growing?
Demand is rising fast: Naukri JobSpeak reports AI/ML job postings up 33% YoY in July 2026 (25% in June), and job postings asking for LangChain, CrewAI or 'AI agent' skills grew over 300% between Jan 2025 and Mar 2026. The skill mix is shifting from plain RAG toward agents, evals and protocols like MCP.
Do I need a degree or a paid certificate for this?
Nothing on this page requires a paid certificate, and none of the 25 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
How much demand
What each job portal shows for this role's title — the readings behind the openings figure above.
- 6dated job postings for this role we read in the last 60 days — the figure above, because no portal's count reached it
Where this role is heading
- Demand is rising fast: Naukri JobSpeak reports AI/ML job postings up 33% YoY in July 2026 (25% in June), and job postings asking for LangChain, CrewAI or 'AI agent' skills grew over 300% between Jan 2025 and Mar 2026.
- The skill mix is shifting from plain RAG toward agents, evals and protocols like MCP.
Capability percentages come from 25 job descriptions read in full on 24-08-2026. How we do this