AI & Data Talent
Hire LLM Engineers
Our LLM engineers build dependable language-model features, from retrieval over your own content to tool-using assistants, with testing and guardrails included.
Overview
Hire LLM Engineers
LLM engineers specialize in making large language models useful inside real software. That means prompt and context design, retrieval over your documents, tool calling, structured outputs and, importantly, evaluation. A demo that impresses once is easy; a feature that behaves consistently across thousands of inputs takes engineering. Everything is documented as we go, so the knowledge stays with your team rather than with individuals.
We integrate with your existing application and cloud setup, and we are open about trade-offs such as cost, latency and data exposure. Engagements can be dedicated, part-time or project-based. You get regular demos and written decisions so your team understands how the system works. You stay informed throughout, with clear explanations of what is being done and why it matters.
Context
When to hire
- 01
Your prototype is unpredictable
Answers vary or drift off-topic. Structured evaluation and guardrails can make behavior consistent.
- 02
You need answers grounded in your content
Public models do not know your documents. Retrieval pipelines connect the model to approved sources.
- 03
Costs and latency are rising
Usage is growing and bills with it. An engineer can optimize prompts, caching and model choice.
- 04
Security and privacy questions are blocking launch
Stakeholders want clarity on what data reaches which provider, and how outputs are checked.
Capabilities
Skills you get
- 01
Retrieval-augmented generation
Chunk, embed and index your content, then retrieve and cite relevant passages.
- 02
Prompt and context design
Write maintainable prompts and manage context so responses stay accurate and on task.
- 03
Tool calling and agents
Let models call your APIs and services safely, with limits on what they may do.
- 04
Evaluation harnesses
Build test sets and automated checks so every change can be measured against the last.
- 05
Guardrails and safety
Validate outputs, filter sensitive content and add human review where it matters.
- 06
Cost and latency tuning
Choose models by task, add caching and streaming, and monitor usage.
Typical work
- Internal knowledge assistants
- Support answer drafting
- Document summarization and extraction
- Natural language interfaces to data
- Workflow automation with tool-using agents
Tools they use
- OpenAI API
- Anthropic API
- LangChain
- LlamaIndex
- pgvector
- Pinecone
- Python
- TypeScript
Approach
How hiring works
- 1
Share your needs
Tell us about your project, stack, team setup and the skills you are looking for.
- 2
Review the match
We suggest developers whose experience fits, and you can talk with them before deciding.
- 3
Onboard
Access, tools, communication channels and working hours are agreed up front.
- 4
Build & review
Work runs in your process with regular updates, and the team can scale as needs change.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
Which models do your engineers work with?
They work with major hosted model APIs and open models, and we help you choose based on quality, cost, latency and data requirements rather than defaulting to one provider.
How do you reduce incorrect answers?
We ground responses in your content, add validation, and measure quality with test sets. No system is perfect, so we also design review steps for higher-risk outputs.
Will our data be used to train third-party models?
That depends on the provider and plan chosen. We review provider terms with you and design the data flow to match your policies.
Who owns what gets built?
Ownership is defined in the agreement, typically assigned to the client.
Explore
Keep exploring
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.