Hire AI developers in India who ship systems, not demos

Which AI developer do you actually need?

“AI developer” covers at least six jobs. Hiring the wrong one is the most common and most expensive mistake we see.

For most first projects the answer is one LLM application engineer, with a data engineer if documents are messy. Python is the usual language for ML and data work; our Python development page covers frameworks and versions.

Production AI engineers vs demo builders: the skills that separate them

How to vet AI developers: three trial tasks

Each task fits in two to four hours. Pay for anything longer. Use your own sanitised material where you can, so the result also teaches you something about your problem.

1. Improve a failing RAG pipeline against an eval set

Give a small repository: a few hundred documents, a naive retrieval pipeline and around thirty questions, each with the passage that should be found. Tell them the current pass rate and ask them to raise it. Good work measures retrieval on its own first, finds a cause (tables split across chunks, no metadata filter, keyword queries that pure vector search misses), and reports before and after numbers. Weak work edits the prompt and declares victory. Our guide to RAG in production lists the usual culprits.

2. Debug an agent trace

Hand over an illustrative JSON trace in which an agent looped for fourteen steps, picked the wrong tool twice and issued the same refund two times. Ask what went wrong and what they would change. Strong candidates point to overlapping tool descriptions, a write tool with no idempotency key, no step cap, and tool output that contained instructions the agent obeyed. They fix it in code and tests, not only in the prompt.

3. Estimate cost per task

Give monthly volume, average steps per task and tokens per step, and let them look up the provider’s current price page. Good answers show the formula, add context growth across steps, retries and the long tail, then end with a range and the two assumptions that move it most. Our AI agent cost guide walks through the same arithmetic.

Interview questions for generative AI developers, with good-answer notes

• “How would you know a prompt change made things better?” Good: run the eval set before and after, read the failures rather than only the average, and repeat runs because outputs vary.
• “Our assistant gives confident wrong answers. Where do you look first?” Good: check whether the right passage was retrieved at all. If not, fix retrieval; if yes, fix grounding, add citations and an “I don’t know” path.
• “How do you stop an agent doing something expensive or irreversible?” Good: scoped tools, approval before writes, idempotency keys, step and spend caps, and no irreversible actions exposed as tools.
• “A provider ships a new model version. What do you do?” Good: run the regression evals, compare cost and latency, roll out gradually, keep the old version pinned until the numbers agree.
• “How do you handle personal data in prompts and logs?” Good: send the minimum, redact in traces, set provider retention options, keep keys server-side, and check the processing contract.
• “When would you fine-tune instead of using RAG?” Good: for consistent format, tone or a narrow classification at volume, once evals show prompting cannot reach the target. Not for knowledge that changes weekly. See LLM fine-tuning for the trade-offs.

Red flags when hiring AI developers

• A portfolio of notebooks and demo videos with no evals, traces or production numbers.
• Cannot name a single failure mode they measured and fixed.
• Puts API keys in front-end code, or shares them in chat.
• Believes hallucination is solved by a better prompt.
• Proposes a multi-agent architecture for a task with one decision in it.
• Suggests fine-tuning first when users need answers from changing documents.
• Wants to test with your production data on a personal or free-tier account.

Hire AI agent developers: the extra checks

Agent work adds risk because the model chooses actions. On top of the kit above, ask the candidate to write the tool list for your task with each tool’s permission, which tools need human approval, and the conditions under which the agent must stop and hand over. Ask how they would replay a customer complaint from traces. If they cannot explain why most agent ideas should start as a workflow, keep looking. Our AI agent development page sets out the permission model we use.

Data protection when an external AI developer touches your data

• Keys stay server-side and in your account. The model provider account belongs to your organisation, not the developer. Use separate keys per environment with spend limits, and give the developer a non-production key.
• Personal data is masked by default. Develop on synthetic or masked data; redact before anything reaches traces or logs; give access through your VDI or a managed device if your policy says so.
• DPDP applies to the whole chain. The Digital Personal Data Protection Rules were published in the Gazette on 13 November 2025 with an eighteen-month phased compliance period. Under the DPDP Act a data fiduciary may use a data processor only under a valid contract, so your agreement with any vendor, and theirs with the model provider, must cover purpose, safeguards and deletion. Our DPDP checklist lists the controls to build.
• Provider retention is a setting, not an assumption. Anthropic’s API retention documentation says retained data is not used for training without your express permission, offers zero data retention by arrangement, and notes that stateful features such as batch jobs, file storage and code execution keep data under their own policies; some newer models require 30-day retention. OpenAI’s data controls page says API data is not used for training by default, abuse-monitoring logs are kept for up to 30 days, and zero retention needs approval. Decide these settings before a developer starts, as of November 2026.
• Ownership is written down. The contract should assign to you the prompts and system instructions, eval sets and labels, traces, fine-tuning datasets and any fine-tuned weights or adapters. Run fine-tuning jobs in your organisation’s provider account so the resulting model never sits in the vendor’s.

Tracing helps here too: OpenTelemetry publishes GenAI semantic conventions for model and agent spans, so traces can go to the observability stack you already control instead of another vendor’s dashboard.

Hiring AI developers in India for Dubai, UAE or US teams

For teams in Dubai and the rest of the UAE, India is only 1.5 hours ahead, so the working days overlap almost completely. Check the UAE’s federal data protection law and, inside DIFC or ADGM, those free zones’ own rules before sharing customer data, and test Arabic or mixed-language inputs in your evals if users write that way. Our AI agent development in Dubai page covers the local picture. For US teams the gap is 9.5 to 13.5 hours depending on coast and season. That works when specs, eval dashboards and handovers are written, with a short shared window each day for decisions.

What drives the cost of hiring AI developers in India

We do not publish rates, and we are wary of anyone quoting one figure for “an AI developer”. The real drivers:

• Production experience. Engineers who have run evals, traces and cost budgets on live systems are scarcer than those who have built demos.
• Specialism. Training and serving models on GPUs is a different market from building on model APIs.
• Overlap hours. Late-evening shifts for US teams cost more than a standard Indian day.
• Security requirements. VDI-only access, background checks and on-site days add effort.
• Model and API spend. That bill is yours, separate from the engineer’s rate, and it belongs in the business case from day one.

If an AI developer will deploy to your cloud, pair them with someone who owns the platform. Our guide to hiring DevOps engineers in India covers that role and the access rules for outside engineers.