ML Engineer

ML Engineer (id 117681) Senior "Customer location: Germany Candidates locations: Germany Language: English B1+, German B2 Estimated start date: 30.09.2026 Duration: 3 months"

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)

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