LLM Engineering

Hire LLM Engineers

Hire LLM engineers who build reliable applications on large language models - RAG, fine-tuning, evaluation and private deployment on Claude, GPT and open models. Engineers who ship accurate, cost-controlled LLM features, contributing within 48-72 hours.

Hire LLM Developers See Rates

What our LLM developers bring

  • check_circle RAG pipelines and retrieval quality
  • check_circle Fine-tuning (LoRA / PEFT) and adapters
  • check_circle Prompt engineering and evaluation harnesses
  • check_circle Vector databases (pgvector, Pinecone, Weaviate)
  • check_circle Private and self-hosted LLM deployment
  • check_circle Latency, throughput and cost optimisation

What they build

LLM applications

Chat, search and workflow features built on LLMs.

Knowledge assistants

RAG systems grounded in your data, with citations.

Evaluation harnesses

Test suites that keep model quality measurable over time.

Private deployments

Self-hosted open models where data can't leave your walls.

Flexible ways to hire

Bring on LLM talent through a dedicated team, staff augmentation or a fixed-price project — whichever fits your roadmap. See typical developer rates or browse all expert teams.

Hire vetted LLM developers

Tell us what you need and we'll match you with senior LLM engineers, often within 48–72 hours.

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Frequently asked questions

How quickly can I hire an LLM engineer? expand_more
Most clients have a vetted LLM engineer contributing within 48-72 hours of sharing requirements.
What is the difference between RAG and fine-tuning? expand_more
RAG supplies facts at answer time from your data (fast to update); fine-tuning changes the model's style or behaviour. Our engineers advise on the right mix and often use both.
Can they deploy private or self-hosted models? expand_more
Yes. For sensitive data we deploy open models (Llama, Mistral) privately or self-hosted, with the same evaluation and guardrails as hosted models.
How do LLM engineers control cost and latency? expand_more
Through model selection, caching, retrieval tuning, prompt/token optimisation and routing between models, so quality stays high while cost and latency stay predictable.