To hire LLM developers in India who can ship, test for three things a CV cannot show: whether they build evaluation sets before tuning prompts, whether they can debug a retrieval pipeline that returns the wrong chunks, and whether they know when fine-tuning is worth it. Then choose an engagement model that ties payment to working milestones.
If you would rather skip the screening and put senior engineers on your use case directly, describe what you want the model to do and we will propose a team and a first milestone.
Why a demo is not proof
Almost anyone can wire an API key to a chat window in an afternoon. The difficult part of LLM engineering starts after the demo: the answer that is right 80 times and confidently wrong the 81st, the retrieval step that misses the one clause that matters, the latency that doubles under load, the monthly token bill nobody forecast. The candidates worth hiring have lived through those problems and can talk about them in detail. Our broader guide to hiring generative AI engineers in India covers the wider role; this article focuses on the LLM application engineer specifically.
Skills to test when you hire LLM developers in India
Evaluation discipline
This is the single best predictor of a strong LLM engineer. Ask how they would know if a change made the system better or worse.
- Do they build a golden dataset of real questions with expected answers before iterating?
- Can they explain the difference between exact-match checks, rubric-based LLM-as-judge scoring and human review, and when each is trustworthy?
- Do they run evals automatically on every prompt or model change, like unit tests?
Prompting combined with retrieval (RAG)
Most business LLM systems answer from your own documents. Probe the full pipeline:
- Chunking strategy and why chunk size changes answer quality.
- Hybrid search (keyword plus vector) and re-ranking.
- Metadata filtering for access control, so a user never retrieves documents they should not see.
- Citation of sources, and refusing to answer when retrieval comes back empty.
A useful debugging question
"Users say the bot gives outdated policy answers. Walk me through how you would find the cause." Strong candidates check the index freshness, inspect retrieved chunks for the failing queries, look for duplicate old documents, and only then touch the prompt. Weak candidates rewrite the prompt first.
Fine-tuning judgement
Fine-tuning is sometimes the right call: consistent output formats, domain tone, classification at high volume, or distilling a large model into a smaller, faster one. It is rarely the fix for missing knowledge, which RAG handles better. Ask candidates when they would choose LoRA or full fine-tuning, how they would prepare and de-duplicate training data, and how they would prove the tuned model beats a well-prompted base model on the eval set.
Production engineering
- Structured outputs and schema validation so downstream code does not break.
- Tool and function calling with safe limits on what the model can trigger.
- Caching, batching and model routing to control latency and token spend.
- Guardrails against prompt injection, especially when the model reads user-supplied documents or web pages.
- Tracing and logging of prompts, retrieved context and outputs for later debugging.
A practical interview loop
- Portfolio conversation (45 minutes). Pick one system they built. Ask what broke in production and what they changed.
- Take-home or paired exercise. Provide twenty documents and thirty questions. Ask for a small RAG service plus an eval script that reports accuracy. Review the eval first, then the code.
- Architecture discussion. Give them your actual use case and ask for a design, including where humans stay in the loop and how cost scales with usage.
- Security and data handling. Ask how they would keep customer PII out of third-party model logs, and how they would handle India's DPDP Act requirements for consent and purpose.
How to structure the engagement
How you engage matters as much as who you hire. Three common options:
Freelance specialists
Useful for a narrow task such as an eval harness or a proof of concept. Risky for anything customer-facing, because continuity and support are not built in.
Dedicated developers through a firm
You get named engineers who work in your stand-ups and repositories. Insist on seniority you can verify, code ownership from day one, and the ability to replace someone quickly if the fit is wrong.
Milestone-based project team
Best when you want an outcome rather than headcount. Define milestones by measurable results, for example "answers 90 of the 100 golden questions correctly with citations", rather than by hours logged. This is how iJurug Soft typically works: a Bangalore studio operating since 2018, where our LLM and RAG engineering services are delivered by senior engineers through fixed milestones (Discover, Design, Build, Launch and grow) followed by long-term support. For a sense of what full product builds involve, see our overview of generative AI development in India.
Contract clauses worth including
- Ownership of prompts, eval datasets, fine-tuned weights and code.
- Where data may be processed, and which model providers are allowed.
- Handover documentation and a runbook for the eval pipeline.
Frequently asked questions
What is the difference between an LLM developer and an ML engineer?
An ML engineer usually trains models from data. An LLM developer mostly builds applications on top of foundation models: retrieval, prompting, tool use, evals and deployment. Senior people often do both, but test for the one you need.
Should the team know open-weight models as well as hosted APIs?
Ideally yes. Self-hosting models such as Llama or Mistral with vLLM matters when data cannot leave your environment or volume makes per-token pricing unattractive.
How fast can an LLM team start?
That depends on availability and scope. The quickest starts come from a clear use case, sample documents and a list of real user questions ready on day one.
How is an LLM engagement priced?
We do not publish rates. Team size, duration, integration depth, hosting approach and support needs all shape the quote, so share your scope for a clear proposal.
Want experienced LLM engineers without months of hiring? Write to info@ijurugsoft.com or brief us through the contact form with your use case and a few sample questions. A senior engineer will reply with a suggested team shape and first milestone.