Dedicated LLM engineers for your offshore team.
Offshore LLM engineers in Chiang Mai and Bangkok, working full time on your product 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.
We employ, manage and keep training the engineers we assign, and you pay one fixed monthly fee.
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 area 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 oversees delivery, training and coaching, the office and equipment, and HR and payroll. The price below is for a junior LLM Engineer; other levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One junior LLM Engineer, full time, everything below included. All 27 things included ››
- LLM engineer From USD 3,100 per month
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Service
We manage and develop the team
- A service delivery manager who oversees operations and 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 team member
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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 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. With your own engineers, they work 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.
Junior to lead. The role is system design around the model, well beyond prompt writing, and senior engineers carry the hardest of that design. If that describes your problem, plan for a senior when you read 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. The LLM engineer sits alongside an AI or NLP engineer in 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 you would think. Retrieval and context engineering usually come first, because they're cheaper to change and often close most of the gap. Fine-tuning is the right tool 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 which AI features you run, what they cost per month and where the answers fall short. We'll suggest the level of LLM engineer that fits and send the monthly fee. You can talk to a service delivery manager first.
Tell us what your LLM engineer would work on.
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