Hire LLM integration engineers
Senior LLM integration engineers who build products on top of large language models, including prompt pipelines, tool calling, gateways and guardrails. The assessment leaves model training aside and tests whether they catch a confident model that's plainly wrong.
01
Prompt engineering became a real discipline
Engineers now design and version prompts the way they once designed APIs, and a poorly scoped prompt costs real money at scale. The work involves structured outputs, few-shot patterns, and retrieval augmentation, so engineers who treat prompts as an afterthought ship brittle products.
02
Tool calling changed how agents fail
Features that call external tools can fail in ways that look like success, with the model confirming an action it never completed. Engineers now build verification steps and fallback paths into every tool interface, alongside the happy-path logic.
03
Guardrails moved into the application layer
You can't rely on a model provider's safety filters alone, so engineers build output classifiers, rate limits and cost controls directly into the application. That work now sits in your product code, where your team owns and tests it.
How we assess LLM integration engineers
Every engineer works through a fundamentals assessment with AI tools switched off and a second assessment with their own agents running. For LLM integration engineers, these criteria carry the most weight.
- AI output verification
The engineer is shown a confident model response that contains a subtle factual error and must write the verification logic that would catch it before it reaches the user.
- Prompt and context engineering
They're given a real-world prompt that produces inconsistent outputs and must diagnose why, then rewrite it with explicit constraints and test their fix against edge cases in the codebase.
- Tool orchestration
They design a tool-calling sequence in a live codebase where one tool's output feeds the next, and we check that they handle partial failures without the model silently swallowing the error.
Hiring with Miyagami
- One senior engineer, full-time, at a fixed monthly fee
- Employment, payroll and compliance included, no recruitment fee
Timeline
- Three-month minimum
- Wrong fit: we replace at our cost
What is RAG
RAG connects a language model to an external knowledge source at inference time, so answers draw on retrieved documents rather than training data alone.
What is an eval
An eval is a test that measures how well a language model or AI system performs on a defined task. Teams use evals to check whether a change makes the system better or worse.
What is an AI gateway
An AI gateway is a proxy layer that sits between your application and one or more model APIs, handling routing, auth, rate limits, logging and cost controls.
Questions about hiring LLM integration engineers
How senior are these engineers?
The LLM integration engineers we put forward are senior, with 6+ years' experience on average, and they've shipped and maintained production LLM integrations. Every one has passed both assessments, and you see their scorecards before you meet them.
How do you test LLM integration specifically?
Engineers work through both assessments in a real codebase, the first with AI tools off and the second with their own agents on. One scenario gives them a confident but wrong model answer, and we watch how they catch and handle it.
How quickly can one start?
You'll have a shortlist within five working days of briefing us. From shortlist to first commit is typically three weeks. Engineers work full-time from day one, with a three-month minimum engagement.
What does an LLM integration engineer cost?
You pay a fixed monthly fee of €5,600 to €8,000 per full-time engineer, with employment, payroll and compliance included and no recruitment fee. The pricing page compares it with the all-in cost of a senior local hire in your country. Engagements are full-time with a three-month minimum, and if a placement doesn't work out, we replace them at our cost.
Do they use our internal tools or their own?
They adapt to your stack. In the vetting assessment they bring their own AI agents, so we know they can work productively with any tooling. In practice most integrate with whatever gateway, observability, or prompt-management tools your team already uses.
Get a shortlist within five working days
You share the roles and the stack in a short form or a thirty-minute call. Within five working days you get named senior engineers to review, each with both scorecards.
ISO 27001Certified