Hire AI and ML engineers
Hire AI engineers who ship to production, not just to a notebook
AI, ML and GenAI postings in India grew 68% year on year in 2026. Plenty of candidates can build a demo. We look for engineers who can make it reliable, measurable and affordable once real users arrive.
An AI or ML engineer builds software that uses machine learning models. In 2026 that mostly means integrating large language models (LLMs) into products: retrieval augmented generation, agents and tool use, prompt and output design, evaluation, and the infrastructure to run it all reliably. Some roles also involve training or fine-tuning models.
TechNimbus supplies AI and ML engineers from India on contract and dedicated terms, and builds AI features as projects.
Two profiles
LLM application engineer or ML engineer?
Most companies in 2026 need the first. Make sure you are hiring the right one.
LLM application engineer
- Integrates OpenAI, Anthropic and open models into products
- Builds RAG pipelines, agents and tool calling
- Designs evaluation and guardrails
- Strong backend engineering in Python or TypeScript
Machine learning engineer
- Trains, fine-tunes and evaluates models
- Works with PyTorch and data pipelines
- Handles feature engineering and MLOps
- Strong maths and data foundations
How we vet
What we test AI engineers on
AI interviews are where hype is easiest to spot. We test for:
- Retrieval: chunking strategies, embeddings, hybrid search and why a RAG system returns the wrong passage.
- Evaluation: building a test set, scoring outputs and proving a change made things better.
- Failure modes: hallucination, prompt injection, data leakage between users, and how to design against them.
- Cost and latency: model selection, caching, streaming and token budgets.
- Engineering: the same backend standards we expect from any senior developer, because AI features are still software.
- Judgement: knowing when a problem does not need an LLM at all.
Every developer passes a technical interview with a senior engineer on our side before you see their profile. You then interview them yourself.
Market rates
AI and ML engineer rates in India, 2026
| Role / stack | Junior (0–2 yrs) | Mid (2–5 yrs) | Senior (5–8 yrs) | Lead (8+ yrs) | Dedicated senior, monthly |
|---|---|---|---|---|---|
| AI / ML engineer (PyTorch, LLMs, RAG) | $22–35 | $40–65 | $60–95 | $85–120+ | $9,600–15,200 |
| Data engineering (Snowflake, Spark, dbt) | $20–32 | $34–56 | $50–80 | $70–100 | $8,000–12,800 |
Jellyfish reports that senior talent in generative AI, RAG architectures and complex cloud work commands up to a 40% premium over traditional application development. Third-party estimates, not TechNimbus prices. Sources: Jellyfish Technologies, CompanyBench Research.
What skills should an AI engineer have in 2026?
Strong software engineering in Python or TypeScript, hands-on experience integrating LLM APIs, retrieval augmented generation, evaluation methods, and an understanding of security risks such as prompt injection. For model training roles, add PyTorch, data pipelines and MLOps.
Can an AI engineer work with our existing product team?
Yes. Most of our AI engineers join existing teams to add AI features to products that already exist, working with your backend and frontend developers.
Do you place engineers who can fine-tune models?
Yes, when the role calls for it. Many problems are better solved with retrieval and good prompting than fine-tuning, and we will say so if that is the case for yours.
Why are AI engineers more expensive?
Demand is growing faster than supply. CompanyBench reports AI, ML and GenAI postings in India up 68% year on year in 2026, and Jellyfish puts senior AI and ML engineers at $60 to $95 an hour against roughly $42 to $75 for other senior development roles.
Should we hire an AI engineer or commission an AI project?
Hire an engineer if you have product direction and expect continuous AI work. Commission a project if you have one defined problem and want someone accountable for delivering the outcome.
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Send a short brief. We reply within 48 hours with a clear plan, a realistic timeline and an honest view on whether we are the right team for it.