Hire AI engineers
in India
Senior AI engineers who have already shipped an LLM feature that survived real traffic, not just a weekend demo. Integrate GPT-4o, Claude, or Gemini, ground answers with RAG, or build an agent that takes real action, matched in 48 hours at 60 to 75 percent less than a comparable US or UK hire.
What our AI engineers build for you
An AI engineer is your generalist, someone who can integrate a model into your product end to end. Six kinds of work come up most, and each one has grown deep enough to be its own specialty. Start here, and we route you to a specialist page when the depth calls for it.
Autonomous AI agents that take action
Agents that search the web, query your database, call an internal API, and complete a multi-step task with a human-approval step where the action carries real risk.
AI agent developers →RAG systems grounded in your data
Retrieval pipelines that pull from your documents, wikis, or tickets so an answer cites a real source instead of the model's best guess.
RAG engineers →Production ML pipelines and model ops
Deployment, monitoring, and retraining infrastructure so a model that works in a notebook keeps working six months into production.
MLOps engineers →Prompts tuned for accuracy, not guesswork
Structured prompting, few-shot examples, and an eval harness that measures whether a prompt change actually improved the output before it ships.
Prompt engineers →Predictive models trained on your own data
Classification, forecasting, and recommendation models built and evaluated against your historical data, not a generic off-the-shelf model.
Machine learning engineers →Generative features across content, code, and images
Content generation, code assistance, and image or video pipelines built on foundation models and tuned for your product's voice and format.
Generative AI developers →What a senior AI engineer actually does
Calling an LLM API is an afternoon of work. Shipping an AI feature that holds up under real traffic, real cost pressure, and real users typing things you did not anticipate, is a different job entirely. Here is what that job covers.
Wiring an LLM into a real product
Streaming responses so a user sees text appear instead of staring at a spinner, managing the context window so a long conversation does not silently truncate, and choosing between GPT-4o, Claude, and Gemini based on latency, cost per token, and how each one actually performs on your specific task rather than a generic leaderboard. Function calling and tool use, letting the model request a database lookup or an API call mid-conversation, is now table stakes rather than an advanced feature. A senior engineer also builds the fallback path: what happens when the primary model is slow or the API returns an error, because a feature that has no answer for that is a feature that goes down on launch day.
Grounding answers with retrieval
RAG, retrieval-augmented generation, is how you stop a model from answering a question about your product with something it half-remembers from training. It means chunking your documents, generating embeddings with a model such as OpenAI's text-embedding-3 line or an open-weight option like BGE, indexing them in a vector database (Pinecone, Weaviate, Qdrant, or pgvector if you are already on Postgres), and re-ranking the results before they ever reach the model. This is deep enough work that it has become its own specialty. If grounding is most of your roadmap, the RAG engineer page goes further into it.
Fine-tuning versus prompting — a build decision, not a preference
Prompting is faster to iterate on and costs nothing beyond the API call, so most teams start there: a good system prompt, a handful of examples, and clear output-format instructions get you further than expected. Fine-tuning, adjusting a model's weights on a hand-labeled dataset with techniques like LoRA or QLoRA, earns its cost when prompting hits a ceiling, usually a consistent format problem or a narrow domain task where a smaller, tuned model beats a bigger general one on both speed and price. A senior AI engineer treats this as a measured trade-off, tested against real examples, not a default they reach for out of habit.
Model serving: API, self-hosted, or both
For most products, calling OpenAI or Anthropic's API is the right call, no infrastructure to run and a model that improves without you doing anything. Self-hosting an open-weight model, LLaMA or Mistral, through vLLM or Ollama, or training a model from scratch in PyTorch or TensorFlow, makes sense once volume is high enough that per-token API cost stops making sense, or once data residency rules mean the model has to run inside your own infrastructure. Cloud options like AWS Bedrock sit in between, giving you a managed environment for models you would otherwise have to host yourself. Knowing which of these three fits your actual usage pattern, not the one that sounds most impressive, is a core part of the job.
Evals: the difference between a demo and something you can ship
A demo works on the five examples you tried it on. Production needs a test set of real questions with known-good answers, run automatically every time a prompt, model, or pipeline changes, so you catch a regression before a customer does. Tools like LangSmith, Langfuse, and Arize track this over time and flag drift, a model quietly getting worse at your specific task as usage patterns shift. Every AI engineer we place is expected to build this discipline into a project from week one, not bolt it on after something goes wrong in production.
When to call an API and when to train your own model
Default to an API for anything that is not your core product differentiator, it ships faster and improves for free as the provider updates the model. Consider training or fine-tuning your own when you are running high enough volume that unit economics tip in favor of a smaller self-hosted model, when the task is narrow enough that a small tuned model genuinely beats a large general one on latency, or when data cannot leave your infrastructure under any circumstances. Getting this call wrong in either direction is one of the most expensive mistakes we see in AI projects, months spent training a model an API would have handled, or months spent fighting API cost and latency on a workload that should have been brought in-house from the start.
Hire AI engineers in India by specialty
Five domains inside AI engineering come up often enough that we run a dedicated screen for each. Read the summary, then follow the link for depth.
Hire AI engineers in India for RAG and LLM systems
RAG and LLM integration are two sides of one problem: getting a model to answer with your facts, not its training data. LLM integration wires a model like GPT-4o, Claude, or Gemini into your product, the API call, the streaming response, the system prompt. RAG sits on top, pulling relevant passages at query time.
An LLM developer owns the integration layer: context-window management, function calling, a fallback for when the provider is down. A RAG engineer owns retrieval underneath it: chunking, embeddings from a model like OpenAI's text-embedding-3 or an open-weight option such as BGE, a vector index in Pinecone, Weaviate, or pgvector, and a re-ranking step before any passage reaches the model. Both share one discipline: an eval set checked on every prompt or index change.
Invented or stale answers are a retrieval problem, hire for RAG. Thin integration, no streaming, no fallback, is an LLM problem. Most systems need both. The RAG engineer page covers chunking; the LLM developer page covers integration.
Hire AI engineers in India for computer vision
Computer vision is model work aimed at images and video rather than text: detecting objects in a frame, classifying a photo, segmenting a scene pixel by pixel, or reading text off a scanned document. It runs on convolutional networks and, increasingly, vision transformers, trained on labeled image data.
A computer vision engineer builds the full pipeline: annotating training data in a format like COCO or YOLO, augmenting it so the model generalizes past the training set, training or fine-tuning in PyTorch or TensorFlow, and evaluating against precision, recall and IoU. A model that scores well in a notebook still needs a latency budget once it runs in production.
Real-time and batch are different hires. A warehouse camera flagging a safety issue needs someone who thinks about frame rate on constrained hardware; a backlog of product photos cares more about accuracy than latency. Say which one you are building. The computer vision engineer page goes deeper into formats and edge deployment.
Hire AI engineers in India for NLP
Natural language processing covers tasks where a model has to understand text rather than generate it: classifying a support ticket, pulling names and dates out of a contract, scoring sentiment on a review, or matching a search query to a document by meaning. A smaller model trained for one job is often faster and cheaper at volume than a general LLM.
An NLP engineer fine-tunes a transformer model such as a BERT variant against labeled data for classification or entity extraction, and builds semantic search on sentence embeddings when keyword matching is not enough. The output is judged on precision, recall and F1 against a held-out test set, not on whether a demo answer reads well.
The dividing line with an LLM hire is the task. Extracting information at low cost per item points to NLP; generating open-ended text points to an LLM integration. Many products need both, an NLP model to route and tag, an LLM to draft the reply. The NLP developer page covers model choice and evaluation.
Hire AI engineers in India for MLOps
MLOps is the operational layer that keeps a model working after launch day. A model that scored well in testing degrades quietly as real-world data drifts from what it was trained on, and someone has to notice before a customer does.
An MLOps engineer builds the model registry that tracks which version is live, a feature store so training and serving use the same definitions, and a CI/CD pipeline that runs the eval set before a new version ships. A drift monitor flags when incoming data looks different from the training set.
MLOps earns its cost once you have more than one model in production, or retraining runs on a schedule. A team shipping a single model that rarely changes can usually skip a dedicated hire here; a team running several with weekly retraining cannot. The MLOps engineer page covers the tooling and pipeline design in full.
Hire AI engineers in India for AI agents
An AI agent takes multiple steps toward a goal instead of answering one question and stopping. Given a task, it decides what to do next, calls a tool such as a database query or an internal API, reads the result, and decides again until the task is done.
An AI agent developer builds the orchestration layer, often with LangChain, LlamaIndex, or a custom loop, defines the tool schema for each function the agent can call, and manages its working memory so it does not lose track of what it already tried. A risky action, sending an email, charging a card, deleting a record, stops for human approval first. The eval question shifts from whether the model answered correctly to whether the agent finished the task safely.
Not every automation problem needs an agent. Answering one question well is RAG or an LLM integration, and agent infrastructure for it adds failure points without adding value. Agents earn their complexity when a task needs several steps: triaging a ticket, checking inventory, then drafting a reply. The AI agent developer page covers orchestration, guardrails and evaluation.
What your AI engineer will do once you hire them
A fair question most staffing pages avoid. Here is the actual work, in roughly the order it happens.
Establish an eval set before touching the model
A set of real questions with known-good answers, drawn from your use case, not a generic benchmark. Every later change gets checked against this set before it ships.
Baseline the current behavior
If something already exists, an old model, a rules-based system, a manual process, they measure how it performs today first. That baseline makes a later improvement claim provable, not assumed.
Version data and prompts alongside code
Prompts and reference data change constantly during development, and losing track of which version produced which result wastes days. Both go into version control, tagged to the eval run they were tested against.
Build the data or retrieval pipeline first
For anything grounded in your data, RAG, classification or search, the pipeline that gets the right data to the model comes before prompt tuning. It decides the ceiling on how good the answer can ever be.
Wire the model integration with a fallback path
Streaming, context-window handling, and a plan for when the primary model is slow or returns an error. A feature with no answer for that goes down the first bad afternoon.
Measure retrieval quality separately from generation quality
When something goes wrong, they can tell you whether the wrong passage was retrieved, or the right passage was retrieved and the model answered badly anyway, two different bugs with different fixes.
Ship behind a flag
A new model, prompt or pipeline change goes to a small share of traffic first, watched against the eval set, before it replaces what everyone uses. Rolling back is a flag flip, not a redeploy.
Monitor drift and cost per request
Once live, they track how the model's real-world performance moves over time, and what each request costs. A model that quietly gets worse shows up on a dashboard, not next month's bill.
Write the failure-mode document
What happens when the model is wrong, slow or unavailable, and what a teammate should do about it without waiting for the person who built it. Most AI features ship without this document.
Join your standup and leave documentation behind
They report progress in your terms, not model jargon. By the time an engagement ends, whoever inherits the project can read the eval results and understand why the system works the way it does.
Technologies our AI engineers use daily
Fluent in the major model APIs and the open-source stack around them, so the choice of tool follows the problem rather than whatever a candidate happens to know.
Why hire AI engineers in India
AI talent commands a premium everywhere right now, which is exactly where the India gap opens widest. Here is the case in numbers, not adjectives.
The cost math for an AI hire
A general US software developer earns a median of $133,080 a year per BLS figures. AI engineers sit well above that baseline: a US-based engineer with real LLM and RAG experience typically runs $150,000 to $220,000 a year, a premium the market pays right now for anyone who can ship AI features that work outside a demo. Stack Overflow's 2025 survey put engineering-manager pay at $200,000 in the US against $52,000 in India, roughly four times the burn for a comparable seniority level, and AI-specific roles track a similar multiple. A senior AI engineer through TechTeamsOnline runs about $3,200 a month, near $38,400 a year, all-in, salary, payroll, compliance, and equipment, with no recruitment or visa fee on top. A lead who owns your model and cost strategy runs about $4,500 a month. Clients typically redirect that saving into a second AI hire, an ML engineer or a prompt specialist, rather than treating it as a smaller invoice for the same one person.
A talent pool growing faster than the roles that need it
India has between 4.3 and 5.8 million software developers, roughly one in eight worldwide, and the pool is growing about 11.2 percent a year against 5.6 percent in the US. AI engineering draws from the same feeder system underneath it: about 2.5 million STEM graduates a year, second only to China, and over a million engineering graduates specifically. That surge means "has shipped a production RAG pipeline" or "has taken an agent past a demo" is a combination that would sit open for months in a US job market. Here it gets a real shortlist within days, not because the bar is lower but because the base population feeding into AI specialization is so much larger.
Quality already proven inside Fortune 500 AI programs
174 of the Fortune Global 500 run 390-plus Global Capability Centers in India, employing more than 950,000 engineers, and a growing share of that work is AI, not maintenance. Microsoft's India Development Center has passed 20,000 engineers, its largest outside Redmond, and runs core AI programs on the ground. JPMorgan Chase employs around 55,000 people in India, its biggest technology hub outside the US, with AI teams inside it. Walmart Global Tech runs AI pricing and supply-chain systems out of Bengaluru and Chennai. SAP and Google run comparable engineering hubs doing the same category of work you would be hiring for. India also holds the world's highest concentration of CMMI Level 5 and ISO 27001 certified firms. The rate you pay reflects cost of living here, not a ceiling on what the engineer can build.
Time zone overlap that keeps eval reviews on schedule
India runs on IST, UTC+5:30. An AI engineer on an 11 AM to 8 PM IST schedule gives a US-Eastern team about 2.5 hours of live overlap each morning, enough for a standup, an eval review, or an urgent unblock. UK clients get closer to 4.5 hours. The rest of the day works in your favor: flag a hallucination or a latency spike at the end of your day, and it is usually diagnosed against the eval set and fixed before your next morning, not just acknowledged in a message.
Your prompts, data, and models stay your IP
AI work touches your most sensitive material by design, customer data feeding a prompt, proprietary documents in a retrieval index, so the IP question comes up on nearly every call. Every engagement runs on a master service agreement with work-for-hire and IP-assignment clauses that vest all prompts, pipelines, and code in you from the first commit, backed by an NDA and India's Digital Personal Data Protection Act 2023, which carries penalties up to ₹250 crore. Self-hosted models and vector databases can run entirely inside your own cloud account, so the underlying data never has to leave infrastructure you already control. Want the fuller case? Read why India, or run your own numbers through the cost calculator.
Rates by seniority, and what each level owns
Seniority in AI engineering tracks how much of the build-versus-API decision someone can make on their own, and how far they can be trusted with cost and model choices without a lead checking the work.
| Level | What they own | From |
|---|---|---|
| Associate | Builds an LLM integration feature against a spec, a chat endpoint or a summarization tool, under review from a senior. Comfortable with API calls, still learning cost and context-window tradeoffs. | $1,800/mo |
| Mid-level | Owns a full AI feature end to end, a RAG pipeline or an agent flow, including error handling and fallback logic, with light supervision. | $2,500/mo |
| Senior | Designs the AI architecture for a whole feature area: model choice, retrieval strategy, and eval approach. Catches a bad prompt pattern before it spreads through the codebase. | $3,200/mo |
| Tech lead | Sets the AI strategy for the product: build-versus-API calls, model serving infrastructure, cost budget, and the eval standard the rest of the team is held to. | $4,500/mo |
All-inclusive figures (salary, payroll, compliance, equipment), no recruitment or visa fee on top. See the full rate card or run your own numbers on the cost calculator.
Engagement models
Choose the model that fits your project stage.
Hourly
Best for a short sprint, an LLM integration, or a single AI feature. No minimum commitment, pause or stop anytime.
Monthly dedicated
An engineer committed full-time to your AI roadmap, 160 hours a month, with daily standups and a 7-day risk-free trial built in.
Dedicated AI team
An AI engineer, an ML engineer, and a data scientist scaled up or down monthly as your AI product roadmap changes.
You manage the roadmap, we manage the employment
Your AI engineer works inside your team, your Jira board, your Slack, your model and cost decisions. On paper, they stay employed by us. Payroll, statutory benefits, a laptop, and leave are handled on our end, not yours, and you never need to open an entity in India to make any of this legal.
That split matters more on an AI hire than most, because the tooling shifts every few months. You get a full-time specialist who keeps up with the field because that is their job, without the paperwork, cost, or exit risk of actually employing someone in another country.
How building a team in India works →You own
- Model and cost decisions
- The AI roadmap and priorities
- Eval standards and release calls
- The interview and final yes
We own
- Payroll and taxes
- Benefits and leave
- Hardware and HR
- Free replacement if it slips
The honest answers to the usual worries
AI hiring is newer than general software hiring, so the doubts are sharper. Here are the real ones, answered straight.
"It'll hallucinate and give us wrong answers."
A model with nothing to ground it will invent an answer, that risk is real. What removes it is engineering, not luck: retrieval and re-ranking before the model sees a question, forced citations back to source text, and an explicit fallback when nothing relevant comes back. Every candidate is tested on exactly this discipline, and it shows up in the eval harness before it ever shows up in production.
"The quality won't be production-grade."
The same engineering pool runs core AI programs for 174 Fortune 500 companies across 390-plus centers, Microsoft and JPMorgan among them, and India holds the world's highest concentration of CMMI Level 5 and ISO 27001 certified firms. Quality tracks the hiring bar and the review standard, not the country. Fewer than one in twenty candidates who apply to us pass our screen, and you still interview the shortlist before anyone starts.
"The time-zone gap will slow us down."
A shifted 11 AM to 8 PM IST schedule gives about 2.5 hours of live overlap with US-Eastern each morning and roughly 4.5 with the UK, enough for a real eval review, not just a status check. The rest of the gap runs in your favor: a bug flagged at the end of your day is usually diagnosed and fixed by your next morning.
"The engineer will churn out from under us."
Attrition at India's top IT firms fell from about 23 percent in FY22-23 to 13 percent in FY25, so the sharpest churn years are behind the industry now. Beyond that, the managed model is the real insurance: if someone leaves, you lose a person for a few weeks, not the role, and we backfill it at no extra cost, with documentation of the pipeline and model choices required as a condition of the engagement.
"We'll lose control of our prompts, models, or data."
Every contract uses work-for-hire and IP-assignment clauses that vest all prompts, pipelines, and code in you from the first commit, backed by an NDA and India's Digital Personal Data Protection Act 2023, which carries penalties up to ₹250 crore for a breach. Self-hosted models and vector databases can run inside your own cloud account, so your data never has to leave infrastructure you control.
In-house vs freelance vs TechTeamsOnline
How hiring an AI engineer through TechTeamsOnline compares to the other two routes.
| Factor | In-house hire | AI agency | TechTeamsOnline |
|---|---|---|---|
| Time to hire | 8–16 weeks | 2–4 weeks (project) | 48 hours |
| Monthly cost | $12,500–$18,300 | $15,000–$40,000 | $1,800–$4,500 |
| Production AI experience | Varies by candidate | Team varies | Verified in screening |
| Dedicated full-time | Yes | Shared team | Yes, exclusive |
| 7-day trial | No | No | Risk-free |
| Free replacement | No (rehire process) | No | Yes |
How we vet AI engineers
Fewer than one in twenty candidates pass our four-stage screen.
Portfolio review
We review AI features actually shipped, LLM integrations, RAG pipelines, or agents, and how they held up once real users hit them.
Technical assessment
Build a small RAG pipeline or agent flow, integrate an LLM with streaming, and solve a prompt-and-eval design challenge.
AI systems interview
Design a production AI system live: model choice, cost controls, evals, and what happens when the model gets it wrong.
Communication fit
English proficiency and async collaboration style, checked directly rather than assumed.
What clients say about our AI engineers
"Our AI engineer built a production RAG system over 50,000 documents in six weeks. Accuracy sits at 94 percent and support tickets dropped 60 percent within a month of launch."
"The LLM integration took three weeks, and streaming, caching, and fallback handling were all there from day one. Nothing about it felt like a first attempt."
"We had an agent auto-classifying and routing 500 customer emails a day in a four-week sprint. The engineer caught two edge cases we had not even thought to test."
Frequently asked questions
Everything you need to know about hiring AI engineers from India.
Start your 7-day risk-free AI engineer trial
Describe the AI feature you need built. Get two or three vetted AI engineer profiles in 48 hours, and pay nothing if the fit is not right after a week.