ML Engineer

id 117681
ML Engineer
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)