Machine Learning Engineer

Position- ML Engineer

Location - Remote

Language: English B1, German B2

Duration: 3 months(Freelance contract)

Project Responsibilities

Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2)

Train models on GPUs, including GPU resource management within Kubernetes

Fine-tune transformers/LLMs, track experiments and models via MLflow

Build classic ML models (XGBoost, CatBoost)

Work with data using SQL Server and DuckDB as a lightweight OLAP solution for efficient in-cluster processing of large datasets

Develop in Python (pipelines, integrations, tooling based on uv)

Ensure code quality: testing, CI/CD (GitLab CI)

Work within a zero-trust / secure-by-default environment (network policies, restrictive container rights)

Candidate's Portrait

Location: Germany

German language at B2 level is a must have An experienced ML/ML Ops Engineer with a strong engineering background, combining:

Hands-on, production-grade ML infrastructure experience (not just notebook-level ML)

Deep proficiency in the Python ecosystem and modern engineering practices

Experience working in regulated/enterprise cloud-native environments

Willingness to quickly ramp up on the client's domain (healthcare/billing data), even without prior background in it

Must-haves

Hands-on experience with Kubeflow Pipelines (KFP v2)

Experience training models on GPUs

Experience fine-tuning LLMs/transformers

Experience with MLflow (model tracking)

Experience with boosting models (XGBoost, CatBoost)

Strong Python skills

Experience with SQL and large-scale data processing

CI/CD experience (GitLab CI preferred), clean code and testing practices

Nice-to-have

Pre-training experience for LLMs (beyond fine-tuning)

GPU orchestration in Kubernetes

Experience in zero-trust environments (network policies, restrictive container rights)

Knowledge of DuckDB

Experience with modern Python tooling (uv)