MLOps Engineer.

Ensures that models not only work but stay reliable in production.

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

An MLOps Engineer builds the infrastructure that turns models into reliable systems: automated training and deployment pipelines, versioning of data and models, monitoring for drift and quality decay, reproducible experiments. They are the link between data science and IT operations.

The role determines the lifespan of your AI investments: without MLOps, models decay silently — data distributions shift, no one notices, and a year later the forecasting model is systematically off the mark. With MLOps, model maintenance becomes routine rather than firefighting.

01

Build an ML platform

From scattered notebooks to a pipeline landscape with versioning, CI/CD and reproducible training — tailored to your cloud and team size.

02

Establish drift monitoring

Automated monitoring of data and prediction drift with clear alerting and retraining rules — before gradual quality decay starts costing money.

03

LLMOps for generative applications

Prompt versioning, eval pipelines and cost monitoring for LLM applications — the same operational discipline as for classic models.

04

Achieve handover readiness

An externally developed model is to be operated by your team: documentation, runbooks, automation and training for in-house operation.

Core competencies
CI/CD for MLData/model versioningMonitoring & drift detectionContainers/orchestrationInfrastructure as Code
Tools & frameworks
MLflow/W&BKubernetes/DockerAirflow/DagsterTerraformCloud ML platforms (SageMaker, Vertex, Azure ML)
Plus factors
LLMOps experienceOn-premise GPU clustersFinOps/cost optimisation
How do you detect model drift before the business feels it?
Core competence: distribution monitoring, proxy metrics and alerting thresholds — anyone who waits for business complaints is not doing MLOps.
How do you make a training run from six months ago exactly reproducible?
Data, code and environment versioning combined — the answer shows whether reproducibility is practised or merely claimed.
What distinguishes LLMOps from classic MLOps in your practice?
Eval instead of accuracy, prompt versioning, token costs — anyone who knows both can operate your entire AI landscape.
How do you size the platform for a three-person data team?
Over-engineering check: Kubernetes cathedrals for small teams are an expensive end in themselves.
What does an MLOps Engineer cost as a freelancer?
Based on our market observations, hourly rates in the DACH region in 2026 range between 105 and 155 euros, depending on platform breadth and LLMOps experience.
When do we need MLOps?
As soon as a model influences business-relevant decisions or more than one model is operated. Discipline is enough for the first pilot; from production operation onwards, monitoring and reproducibility are mandatory.
What is the difference between MLOps and DevOps?
MLOps extends DevOps with the data dimension: in addition to code, data and models are versioned, tested and monitored — because ML systems can degrade without a single line of code changing.
Does MLOps also apply to LLM applications?
Yes — as LLMOps: prompt versioning, evaluation pipelines, cost and quality monitoring. The tools differ, but the operational discipline is the same.

Looking for a MLOps Engineer?

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

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