TL;DR
An AI engineer is the engineer who builds product features on top of large language models: calling model APIs, grounding answers in company data with retrieval-augmented generation (RAG), connecting models to systems through tool calling, and proving the feature works with evaluations. It is a software engineering role at the application layer, not a model training role.
To hire one in India, test for shipped LLM features with measured quality, latency and cost, and source by evidence from AI-native startups, product companies and global capability centres rather than by title.
Key points
- The work is retrieval, tool calling, structured outputs, evaluation and cost control.
- The title is crowded: many CVs claim it on the strength of a chatbot demo.
- The strongest single signal is an evaluation story: how the candidate knew a prompt, model or retrieval change made things better.
- Assess with a practical build on messy documents plus a debugging session on real traces, not an algorithms round alone.
- On SYTalent Market Data, an applied AI engineer earns about ₹22 lakh to ₹40 lakh fixed at three to five years and ₹38 lakh to ₹60 lakh at senior level in 2027.
What the role owns
An AI engineer owns an LLM feature end to end, from the model call to the number that proves it works.
| Area | What they own | How success is measured |
|---|---|---|
| Model integration | API calls to hosted or self-served models, structured outputs, model selection, fallbacks and timeouts | p95 latency, error rate, cost per request |
| Retrieval (RAG) | Chunking, embeddings, hybrid keyword and vector search, reranking, metadata filters, access control | Retrieval recall on a labelled test set, groundedness of answers |
| Tool calling | Function schemas, argument validation, safe and idempotent actions against internal APIs | Tool call success rate, rate of wrong or unsafe actions |
| Evaluation | Golden datasets, automated checks, LLM-as-judge calibrated against human labels, regression gates in CI | Pass rates per release, regressions caught before users see them |
| Guardrails | Prompt injection defences, PII redaction, output filtering | Incidents, red-team findings closed |
| Cost and feedback | Caching, model routing, context trimming; tracing and error analysis | Cost per task, task completion, issues closed from traces |
In smaller companies the same person also covers the work in our agentic AI engineer and AI data engineer guides.
How it differs from adjacent roles
An AI engineer builds the application around foundation models; adjacent roles train models, change them or orchestrate long-running autonomy.
| Role | Primary output | Changes model weights? | Evidence to look for |
|---|---|---|---|
| AI engineer | LLM features in a product: RAG, tool calling, evals | Rarely; occasional light fine-tuning | Features live with eval results, latency and cost numbers |
| ML engineer | Trained and served predictive models from the company's own data | Yes, routinely | Training pipelines, feature stores, model monitoring |
| LLM engineer | Fine-tuned or post-trained language models, model-level optimisation | Yes, it is the job | Fine-tuning runs, preference optimisation, benchmark gains |
| Agentic AI engineer | Multi-step agents that plan, act through tools and recover | Rarely | State, permissions, failure recovery and trajectory evals |
| ## The evidence bar |
Credible proof is a shipped feature, a measurement and a decision the candidate owned. A list of frameworks is not proof.
In the CV, look for:
- Named models and why. Hosted models (OpenAI, Anthropic Claude, Google Gemini, often via Azure OpenAI, Amazon Bedrock or Vertex AI) or open-weight models (Llama, Qwen, Mistral, Gemma) on vLLM, with the reason for the choice.
- Named techniques. Hybrid search with a reranker, query rewriting, structured outputs against a JSON schema, prompt caching, routing between a large and a small model.
- Metrics with a before and after. Retrieval recall, answer accuracy on a labelled set, p95 latency, cost per conversation.
- Ownership. Real users in production, and on-call duty after launch.
In the interview, credible candidates can:
- Walk through their evaluation set: where cases came from, who labelled them, and how LLM-as-judge scores were checked against humans.
- Explain a retrieval failure found in traces and its fix, such as chunking that split tables or a missing permission filter.
- Describe a migration they handled. OpenAI announced that its Assistants API beta would sunset on 26 August 2026 in favour of the Responses API; engineers who moved features across can discuss the trade-offs concretely.
Red flags
These predict a hire who demos well and stalls in production.
- Lists LangChain, LlamaIndex and five vector databases, but no evaluation result.
- Every project is a hackathon, proof of concept or notebook, with no users.
- Proposes fine-tuning as the first fix for weak answers, without first checking retrieval or prompts.
- Treats LLM-as-judge scores as ground truth and has never compared them with human labels.
- Has no idea what the feature cost to run per request or per user.
- Cannot explain how documents a user is not allowed to see are kept out of retrieved context.
Stack and tools
The tools most commonly assessed in India in 2026. Judgement matters more than familiarity.
| Layer | Tools commonly assessed |
|---|---|
| Languages and services | Python, TypeScript, FastAPI, async programming, SQL |
| Model access | OpenAI API, Anthropic Claude API, Google Gemini API, Azure OpenAI, Amazon Bedrock, Google Vertex AI, gateways such as LiteLLM |
| Open-weight models and serving | Llama, Qwen, Mistral, Gemma; vLLM for serving, Ollama for local work |
| Orchestration | LangChain, LlamaIndex, LangGraph, OpenAI Agents SDK, Pydantic AI |
| Retrieval | pgvector, Pinecone, Qdrant, Weaviate, Elasticsearch or OpenSearch, rerankers such as Cohere Rerank |
| Structured outputs | JSON schema structured outputs, Pydantic, Instructor |
| Evaluation and observability | Ragas, DeepEval, promptfoo, LangSmith, Langfuse, Arize Phoenix, Braintrust, OpenTelemetry |
| Guardrails | NeMo Guardrails, Guardrails AI, Llama Guard |
| Cloud | AWS, Azure or Google Cloud, including their managed model services and regional endpoints |
Interview loop that works
Test the actual job: an ambiguous brief, imperfect documents, a quality bar and a cost limit. Keep the loop to about four weeks.
| Stage | What it tests |
|---|---|
| Screen with the hiring manager | Real production LLM work, ownership after launch, motivation for the product domain |
| Past system deep dive | Depth on one shipped feature: architecture, evaluation, incidents, cost |
| Practical exercise | Building under ambiguity, evaluation habits, honest reporting |
| Trace debugging | Diagnosis on failing outputs, the daily work of the seat |
| System design | A production design with a latency budget, a cost budget and a permission model |
| Leadership round | Judgement, communication with product and risk teams, fit with company stage |
A practical exercise that works. Give a small set of policy documents, including a scanned PDF, a table and two documents that contradict each other, plus labelled questions. Ask for a working RAG answerer, an evaluation of retrieval and answers, and one improvement with its measured effect, in three to four hours. Strong candidates report what fails; weak ones report only what works.
Where they come from in India
Most strong AI engineers in India came from backend engineering and moved into LLM work through a product that needed it. Source by what they shipped.
- AI-native startups. The densest pool, mostly in Bengaluru. Screen for evaluation discipline.
- Product companies and SaaS. Engineers who added AI to an existing product bring users and cost pressure.
- Global capability centres. Applied AI teams serving parents; strong on governance, so screen for hands-on depth.
- IT services AI practices. Large pools; screen for production ownership beyond proofs of concept.
- BFSI and FinTech. Document AI and service copilots produce engineers used to permissions and audit.
- Trading firms and research labs. A smaller source for research tooling, strong on engineering.
Bengaluru has the deepest pool, followed by Hyderabad, Delhi NCR and Pune, with Mumbai for BFSI. Our AI jobs in India guide maps the wider market.
What they cost
These are the applied AI engineer rows from our software engineering and AI salaries benchmark, stated as SYTalent Market Data for 2027 fixed pay.
| Level | Typical experience | SYTalent Market Data, 2027 fixed pay, India |
|---|---|---|
| AI engineer, entry | 0 to 2 years | ₹14 lakh to ₹22 lakh |
| AI engineer | 3 to 5 years | ₹22 lakh to ₹40 lakh |
| Senior AI engineer | 6 to 9 years | ₹38 lakh to ₹60 lakh |
| Staff or lead AI engineer | 10 to 14 years | ₹55 lakh to ₹90 lakh |
The reference market is product companies and GCCs in Bengaluru; IT services pay below it, and Big Tech and trading firms above. Agent orchestration or model-level depth moves a candidate into the higher agentic AI or LLM engineer rows.
Hiring in GCCs, banks and regulated firms
In banks, NBFCs and other regulated entities, the hire must handle documentation, approvals and audit trails as well as code.
- FREE-AI. The RBI's FREE-AI committee report of 13 August 2025 set out 7 Sutras and 26 recommendations under six pillars. It is not binding, but it shapes what risk teams ask.
- Model risk. The RBI's draft model risk guidance of 24 June 2026 covers all models used by regulated entities, including third-party and AI models. A hosted LLM is a third-party model, so expect validation and monitoring requirements.
- Personal data. The Digital Personal Data Protection Rules, 2025 reach personal data in prompts, retrieved context and logs, so redaction and trace retention limits matter.
- Data residency. The RBI's 2018 direction on storage of payment system data requires it to be stored only in India, which pushes payment features to India-region endpoints or self-hosted models.
In GCCs, the parent's approved model list usually decides which providers may be used. See AI jobs in fintech and banking for the wider picture.
How SYTalent runs AI engineer searches
SYTalent is a talent search firm working across HFT, FinTech and AI, with 65+ clients and a 50K+ talent network. Our AI, deep tech and quantum practice maps AI engineers by features shipped and evaluated. See our live AI engineer search in Bengaluru, brief us, or browse Roles, including the forward deployed engineer.
Sources
Pay ranges are SYTalent Market Data and are not drawn from these sources.
| Source | Publisher | Date |
|---|---|---|
| FREE-AI committee report, press release | Reserve Bank of India | 13 August 2025 |
| Draft Guidance on Regulatory Principles for Model Risk Management | Reserve Bank of India | 24 June 2026 |
| Storage of Payment System Data | Reserve Bank of India | 6 April 2018 |
| Digital Personal Data Protection Rules, 2025 | Press Information Bureau | November 2025 |
| Assistants API beta deprecation announcement, OpenAI developer forum | OpenAI | 26 August 2025 |
Common questions
What does an AI engineer do?
An AI engineer builds product features on large language models. The work includes calling model APIs, grounding answers in company data with retrieval-augmented generation, connecting models to internal systems through tool calling, enforcing structured outputs, adding guardrails, and building evaluations that show whether a change improved quality. They also own latency and cost in production. Unlike an ML engineer, an AI engineer rarely trains models; the job is the application around them.
How do I hire an AI engineer in India?
Assess for production evidence: a shipped LLM feature, an evaluation set the candidate built, and latency and cost numbers they owned. Run a practical exercise on messy documents and a trace debugging session rather than an algorithms round alone. Source from AI-native startups, product companies, GCCs and FinTech, mostly in Bengaluru, Hyderabad, Delhi NCR and Pune.
What is the salary of an AI engineer in India?
On SYTalent Market Data for 2027, an AI engineer building applied LLM systems earns about ₹14 lakh to ₹22 lakh fixed at entry level, ₹22 lakh to ₹40 lakh with three to five years, ₹38 lakh to ₹60 lakh at senior level and ₹55 lakh to ₹90 lakh at staff level. These figures reference product companies and GCCs in Bengaluru. IT services pay less, while Big Tech and trading firms pay more.
What is the difference between an AI engineer and an ML engineer?
An ML engineer trains, deploys and monitors models built from the company's own data, such as fraud or ranking models. An AI engineer builds applications on foundation models that someone else trained, using retrieval, tool calling, prompts and evaluation. The evidence differs: training pipelines and model metrics for one, shipped LLM features with eval results for the other.
AI engineer vs LLM engineer: what is the difference?
An LLM engineer works on the model itself: fine-tuning, post-training and model-level performance. An AI engineer works at the application layer, building features around models through retrieval, tools and evaluation. Most companies need an AI engineer first, and an LLM engineer only when a hosted model cannot meet quality, cost or residency needs. On SYTalent Market Data, LLM engineers sit in a higher pay band.