LLM Engineer.

Takes language models from experiment into productive operation.

Remote & on-site DACH-wide
LLM Engineer — enquire

An LLM Engineer builds applications on top of large language models — from model selection through prompt architecture and evaluation to productive operation with monitoring and cost control. The role combines software engineering with a deep understanding of how language models behave under real-world load.

It is needed wherever a proof of concept is meant to make the step into production: most AI projects fail not because of the model, but because of evaluation, latency, cost and operational questions — which is precisely the territory of this role.

01

Make a PoC production-ready

An internally built prototype works in demo mode but not under load. The LLM Engineer hardens architecture, evaluation and error handling.

02

Native AI features in your product

Your SaaS product is to gain summarisation, search or assistance functions — as a reliable feature, not a gimmick.

03

Model and provider decision

OpenAI, Anthropic, Mistral or open source on-premise? Structured evaluation against your real data instead of gut feeling.

04

Cost and latency optimisation

Ongoing LLM costs escalate with usage. Caching, routing to smaller models and prompt optimisation reduce them measurably.

Core competencies
Prompt architectureEvaluation (evals)API designPython/TypeScriptObservability
Tools & frameworks
OpenAI/Anthropic APIsLangChain/LlamaIndexvLLMVector databasesMLflow
Plus factors
Fine-tuning experienceOn-premise deploymentsEU AI Act basic knowledge
How do you evaluate whether an LLM feature is good enough for production?
Separates practitioners from demo builders: those who have worked in production talk about eval datasets, regression tests and thresholds — not gut feeling.
Tell me about an LLM project that went wrong — and why.
Real production experience leaves scars: hallucinations at the customer, cost explosions, latency problems. Anyone who cannot name any has only built prototypes.
When would you switch from an API model to a self-hosted model?
Tests architectural judgement: data protection, cost above volume X, latency — the answer should name trade-offs, not ideology.
How do you keep LLM costs under control?
Production readiness shows in the detail: token budgets, caching, model routing, monitoring per feature.
What does an LLM Engineer cost as a freelancer?
Based on our market observations, hourly rates for LLM Engineers in the DACH region in 2026 range between 110 and 160 euros, depending on seniority and demonstrable production deployments.
How does an LLM Engineer differ from a Machine Learning Engineer?
The ML Engineer trains and deploys their own models across the entire lifecycle. The LLM Engineer builds applications on top of pre-trained language models — their focus is on prompt architecture, evaluation and production operation rather than model training.
When do I need an LLM Engineer rather than an ordinary software developer?
As soon as LLM behaviour becomes business-critical: evaluation, hallucination control, cost and latency management require specialist knowledge that conventional web development does not cover.
How quickly is an LLM Engineer available through agentic xperts?
After the initial consultation you will usually receive suitable profiles within a few working days; the project start depends on the candidate's availability — typically two to four weeks.

Looking for a LLM Engineer?

Tell us about your project — within one business day you’ll get an honest assessment of availability, day rate and alternatives.

Request experts