Hire LLM Experts
From India
Fine-tuning, RAG, evals, and production deployment — with engineers who understand what's happening inside the model, not just how to call the API. US, UK & EU companies, served from India.
What our LLM experts build for you
From choosing the right model to deploying it with evals and monitoring — the full stack of LLM engineering, not just the part that looks good in a demo.
LLM Fine-Tuning (LoRA, QLoRA, DPO)
When the base model doesn't fit your domain, tone, or task — fine-tuning is the answer. We run LoRA and QLoRA for parameter-efficient training, and DPO for alignment without RLHF's complexity. Including dataset curation, not just training runs.
Prompt Engineering & System Prompt Design
Structured prompting that produces consistent, parseable outputs at scale — few-shot examples, chain-of-thought, output schemas, and fallback handling. The difference between a demo and something you'd put in front of users.
RAG Architecture & Retrieval Pipelines
Not just a vector store bolted to a chat interface. Chunking strategy, embedding model selection, hybrid search, reranking, context compression — the decisions that determine whether your RAG system actually retrieves the right thing.
LLM Evaluation & Benchmarking
LLM-as-judge, RAGAS, task-specific evals, and regression suites so 'does it work' has a real answer. We build eval pipelines before optimising — otherwise you don't know if changes are improvements.
Model Selection & Cost Optimisation
GPT-4o is not always the right model. We map your task requirements against latency, cost, and capability — and know when Mistral or LLaMA covers 90% of the job at a fraction of the API bill. Routing strategies included.
LLM Deployment & Serving Infrastructure
vLLM and TGI for open-source serving; caching, rate limiting, and fallback chains for API-based systems. Structured for the traffic patterns of a real product — not a notebook that runs once.
Hire LLM experts the way you need them
Staff Augmentation
LLM depth added to your team
An LLM expert embedded in your engineering team. Your architecture decisions improve, your evals get built, and your prompt debt gets paid down — without a full-time hire you may not need in six months.
Best for
Teams already building with LLMs but hitting the ceiling of what they know.
Dedicated LLM Pod
LLM system built end-to-end
LLM expert plus ML engineer — covering model selection, fine-tuning or RAG architecture, evaluation pipeline, and production deployment as a coordinated unit.
Best for
Companies building an LLM-powered product from the ground up.
Project-Based
Scoped LLM engagement
A defined-scope engagement: a fine-tuning run, a RAG pipeline rebuild, an eval framework, or a full LLM feature from design to deployment. Milestones and deliverables agreed upfront.
Best for
Well-scoped LLM problems with a clear definition of done.
Technologies our LLM experts work with
GPT
Claude Sonnet
Gemini
LLaMA
Mistral
DeepSeek
Qwen
LangChain
LlamaIndex
Hugging Face
PyTorch
TensorFlow
Pinecone
pgvector
Chroma
Qdrant
LangSmith
LangFuse
vLLM
MLflow
Python
TypeScript
FastAPI
Docker
SQLWhy global companies hire LLM experts through Artinoid
Calling an LLM API and engineering an LLM system are different skills. One takes an afternoon; the other takes the kind of experience that comes from shipping and iterating in production.
They Know the Internals
There's a wide gap between someone who can call the OpenAI API and someone who understands tokenisation, attention, context windows, and why your model keeps hallucinating the same wrong answer. We hire from the second group.
Faster Than In-House Hiring
LLM engineers with production experience are genuinely hard to find. The ones with fine-tuning and eval track records are rarer still. We've already vetted them — skip the 4–6 month search.
40–60% Lower Than US/UK Rates
Senior LLM expertise out of India at a fraction of US or UK market rates. India's ML research community is deep — we hire from it.
No Lock-In
LLM work tends to be iterative — you learn what the model can and can't do, then adjust scope. Our two-week notice and weekly billing is designed for that reality.
Simple process, faster than in-house hiring
Discovery Call
We learn about your goals, team structure, timeline, and what a successful engagement looks like for you.
Candidate Matching
We shortlist pre-vetted candidates whose skills and experience closely match your requirements.
Technical Interview
You interview shortlisted candidates directly — same process as in-house hiring. You decide who joins.
Contracts & NDA
Agreements signed swiftly. IP assignment, confidentiality, and data handling are all covered before work begins.
Onboarding
Your new team member is set up, briefed, and contributing from day one. No extended ramp-up.
Common questions
Fine-tuning vs prompt engineering — when do you use each?
Prompt engineering first, always. It's cheaper, faster to iterate, and reversible. Fine-tuning makes sense when: the base model doesn't follow your required output format reliably, you need domain-specific vocabulary or tone the model doesn't have, you're running millions of inferences and need to distil a larger model's capability into a smaller one, or latency requirements rule out long system prompts. We won't recommend fine-tuning to increase scope if prompting solves the problem.
How do you handle hallucinations and output reliability?
Hallucination is an architectural problem, not a settings problem. The main levers are: grounding outputs in retrieved context (RAG), structured output schemas with validation, self-consistency sampling, and LLM-as-judge verification for high-stakes outputs. We design for failure — defining what 'wrong' looks like for your use case and building the detection layer before deploying to users.
Can you work with open-source models instead of GPT or Claude?
Yes, and we often recommend it. LLaMA 3, Mistral, Qwen, and DeepSeek cover the majority of production use cases at significantly lower cost — especially after fine-tuning on domain data. We'll map your requirements against the model landscape and give you an honest cost/capability comparison. If the open-source path makes sense, we'll build the serving infrastructure (vLLM or TGI) alongside the model work.
How do you evaluate whether an LLM solution is working?
We build eval pipelines from the start — not as a post-launch checkpoint. For RAG systems we use RAGAS metrics (faithfulness, answer relevance, context recall). For generation tasks, LLM-as-judge against a reference answer set. For classification or extraction, standard precision/recall against a labelled dataset. The eval framework ships alongside the feature — so regressions are caught before deployment.
What's involved in taking an LLM from prototype to production?
More than most teams expect: latency optimisation (caching, streaming, model routing), rate limiting and fallback chains for API-based systems, cost tracking per request, prompt versioning, an eval suite that runs on every code change, and monitoring for output quality drift over time. We scope all of this upfront — not as a surprise after the demo ships.
Can you help reduce our current LLM API costs?
Often, yes. Common wins: routing low-complexity queries to a smaller model (GPT-4o Mini, Haiku), aggressive prompt compression, semantic caching for repeated queries, and replacing API calls with a fine-tuned open-source model for high-volume tasks. We've helped teams cut LLM spend by 40–70% without degrading output quality.
Do your LLM experts sign NDAs before project details are shared?
Yes. NDA and IP assignment agreements are signed before any project specifics are discussed — including your prompts, datasets, and model architecture. Standard for every engagement.
Ready to hire LLM experts?
Tell us where you're at — prototype that needs productionising, a hallucination problem you can't solve, costs you need to cut, or a fine-tuning project you're scoping. We'll match you with the right person.
contact@artinoid.com
Response Time
Within 24 hours
Next Step
Discovery call to scope your LLM requirements