Dedicated LLM engineers for your offshore team.
Offshore LLM engineers in Chiang Mai and Bangkok, working full time on your roadmap and managed by us. An LLM engineer works on everything around the model: context, retrieval quality and inference cost, which is where real-world behaviour is decided.
One agreement and one fixed monthly fee, with a service delivery manager in Thailand who runs your team day to day.
This role is part of a dedicated AI engineering team. It's staff augmentation, fully managed: take one role on its own, or the whole discipline as one delivery team.
One LLM Engineer, assigned to your product and fully managed.
We assign an LLM Engineer to your team full time and run everything around them: a service delivery manager who answers for the work, training and coaching, the office and equipment, and HR and payroll. The price below is for a mid-level LLM Engineer; junior and senior levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One mid-level LLM Engineer, full time, everything below included. All 27 things included ››
- LLM engineer From USD 4,200 per month
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Service
We manage and develop the team
- A service delivery manager who runs the team and answers for delivery
- A 1:1 with our Head of Delivery every two weeks
- Weekly delivery scoring and monthly capacity reports
- A Talent Success Manager for every specialist
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HR
We are the employer
- Recruitment and technical assessment
- Employment contracts
- Salary and payroll
- Tax and social security
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Facilitation
We provide the workplace
- A desk in our own office in Chiang Mai or Bangkok
- A Workplace Experience Manager on site
- Team events through the year
- IT support
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Equipment
We supply the tools
- Laptop and hardware
- Software licences
- LinkedIn Learning access
- Device security and management
What an LLM Engineer does on a dedicated AI engineering team.
They start from the AI features you already run and your own evaluation data, and work with your own engineers on the changes that move quality and cost.
LLM Engineer skills and technologies
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Context and retrieval engineering, including chunking, embedding choice and reranking.
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Fine-tuning and adapter training where prompting has reached its limit.
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Inference optimisation, caching and routing between models to control cost.
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Benchmarking models against your own evaluation set rather than published leaderboards.
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Building the routing logic that sends simple requests to a cheaper model and hard ones upward.
LLM Engineer seniority levels.
Mid-level to lead. The role is system design around the model, well beyond prompt writing, and senior engineers carry the hardest of that design. Seniority is one of the inputs into how the monthly fee is built.
When your team needs an LLM Engineer.
The right addition when an AI feature exists and its quality or cost needs work, alongside an AI or NLP engineer. LLM engineers join as part of a dedicated AI team. It suits companies that hire dedicated developers for a language-model feature and need quality and cost measured, not guessed.
What we look for in a dedicated LLM Engineer.
We look for LLM engineers who can explain why a retrieval result was wrong and fix it. Experience with fine-tuning, embeddings and inference cost matters, and so does the judgement to try the simple fix first.
A remote LLM Engineer's first three months on your team.
- Weeks 1 to 2Benchmarking your current AI features on your own evaluation data.
- Weeks 3 to 6Improving retrieval and context, and routing requests to cut inference cost.
- Months 2 to 3Owning the model layer, including fine-tuning where prompting has reached its limit.
Questions about adding an LLM Engineer to your team.
When is fine-tuning worth it?
Later than many teams expect. Retrieval and context engineering usually come first, because they're cheaper to change and often close most of the gap. Fine-tuning earns its place for a consistent style, a narrow task at high volume, or cost and latency needs a smaller model can meet.
Can you reduce our token costs?
Usually, through caching, routing simple requests to smaller models and trimming the context each call sends. It's one of the most measurable wins available, because token spend shows on every invoice. The engineer sets budgets and alerts as well, so cost stays predictable as usage grows.
Add remote LLM engineers to your team.
Tell us your roadmap and your stack, and we'll come back with the right profile, the capacity and a start date. You can also talk to a service delivery manager first.
Tell us what your LLM engineer would work on.
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We reply within one business day.
