Machine Learning Engineer.

Develops and operates bespoke ML models across the entire lifecycle.

Remote & on-site DACH-wide

A Machine Learning Engineer builds models for tasks that standard AI cannot solve: demand forecasting, anomaly detection, quality control, price optimisation. They take ownership of the entire lifecycle — from data pipeline and feature engineering through training and validation to deployment, monitoring and retraining.

In the age of generative AI the role remains indispensable wherever structured data, measurable predictive quality and hard latency or cost requirements are involved — a language model is rarely the most efficient solution to a forecasting problem.

Forecasting models

Sales, capacity or failure forecasts on your historical data — with honest validation against the naive baseline.

Anomaly detection

Automatically detect fraud patterns, machine failures or quality deviations before they become costly.

Making an ML system production-ready

A Data Scientist's notebook becomes a versioned, monitored, automatically retraining production system.

Deciding classic vs. GenAI

A sober assessment of whether your problem needs a trained model, an LLM or simply good heuristics.

Core competencies

Feature EngineeringModel ValidationML PipelinesPython (scikit-learn, PyTorch, XGBoost)SQL

Tools & frameworks

MLflow/Weights&BiasesAirflow/DagsterDocker/KubernetesCloud ML (SageMaker, Vertex)Monitoring/Drift Detection

Plus factors

Time-Series SpecialisationEdge DeploymentCausal Inference

How do you validate a model against look-ahead and leakage?

The most common cause of "too good" results that collapse in production — anyone who hesitates here hasn't experienced it.

What was your baseline in the last project — and did the model beat it?

Honest ML practice is measured against simple benchmarks; impressive accuracy without a baseline is meaningless.

How do you tell in production that a model is degrading?

Drift monitoring and a retraining strategy separate operators from trainers.

When would you advise against an ML model?

With thin data or unstable processes, heuristics are often better — judgement beats infatuation with method.

What does a Machine Learning Engineer cost as a freelancer?

Based on our market observations, hourly rates in the DACH region in 2026 range between 100 and 150 euros, depending on specialisation and MLOps experience.

Do we still need our own ML models despite ChatGPT and the like?

For forecasting, optimisation and anomaly detection on structured data, trained models are usually more accurate, faster and cheaper than generative AI. The question is not either-or, but which tool fits which problem.

How much data do we need for our own ML model?

That depends on the problem — often a few thousand clean examples are enough. More important than quantity is quality and whether the data reflects what you want to predict. A data-readiness check belongs at the start of the project.

What is the difference between a Data Scientist and an ML Engineer?

The Data Scientist explores data and develops modelling approaches; the ML Engineer turns these into robust production systems with pipelines, deployment and monitoring. Smaller projects combine both in one person.

Looking for a Machine Learning 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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