LLM Engineer assigned to your roadmap.
An LLM Engineer works on the layer around the model. Context assembly, retrieval quality and inference cost, which is where most real-world model behaviour is decided.
Assigned under one service agreement with a minimum monthly capacity in hours, run by a service delivery manager in Chiang Mai, Thailand — five hours ahead of Northern Europe.
This role is delivered as part of our AI engineering services. Take the role on its own, or the whole discipline as one delivery team.
What an LLM Engineer does on a managed AI engineering team.
Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same backlog, the same repositories and the same definition of done as your own team's.
LLM Engineer skills and technology we assign for
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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.
Seniority levels we assign.
Senior. This role is about system design around the model rather than prompt writing. Seniority is one of the inputs in how the monthly fee is built.
When an LLM Engineer is the right assignment.
Assigned where an AI feature exists but its quality or cost has become a problem. On an ongoing roadmap this role is most often assigned alongside an AI Engineer or an NLP Engineer. All 4 roles in this service line can sit on the same agreement, and capacity moves between them at the monthly cycle rather than requiring a new contract. At Azendo this sits inside managed AI services, on the same agreement as the rest of the team.
Questions about assigning an LLM Engineer.
When is fine-tuning worth it over prompting?
Rarely, and later than most teams assume. We would exhaust retrieval and context engineering first, because they are cheaper to change.
Can you reduce our token costs?
Usually, through caching, routing between models and trimming context. It is one of the more measurable wins available.
Add LLM engineer capacity to your roadmap.
Tell us the scope and the stack. We come back with the profile, the capacity and what the first month looks like, or you can talk to a service delivery manager first.
Assign a LLM Engineer to your roadmap.
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