Senior MLOps Engineer | Germany

🧠 Senior MLOps Engineer – Job Description Position: Senior MLOps EngineerLocation: Remote / Hybrid / On-site (customize as needed)Type: Contractor Role OverviewWe are looking for a Senior MLOps Engineer to design, build, and maintain scalable ML infrastructure that powers the full machine learning lifecycle — from experimentation and model training to deployment and monitoring in production.You will collaborate closely with data scientists, AI engineers, and platform teams to ensure that ML systems are reliable, automated, and production-ready. Key ResponsibilitiesBuild and manage MLOps pipelines for data ingestion, model training, deployment, and monitoring.Develop CI/CD workflows for ML models using tools like GitHub Actions, Jenkins, or Argo.Implement model versioning, experiment tracking, and reproducibility using MLflow, DVC, or similar tools.Design and maintain cloud infrastructure for ML workloads (AWS, GCP, or Azure).Integrate feature stores and manage data versioning to ensure consistency across environments.Collaborate with data scientists to operationalize ML models and improve time-to-deployment.Implement monitoring and alerting for data drift, model performance, and system reliability.Optimize training workloads for cost, performance, and scalability (e.g., distributed training, GPU utilization).Ensure compliance, governance, and security across ML pipelines and model storage.Mentor junior engineers and establish best practices for MLOps across teams. Required Skills & Experience5+ years of experience in software engineering, DevOps, or MLOps roles.Strong proficiency in Python, Docker, Kubernetes, and CI/CD systems.Hands-on experience with ML orchestration tools (Airflow, Kubeflow, MLflow, Vertex AI, SageMaker, etc.).Strong understanding of ML model lifecycle management — from experimentation to serving.Deep knowledge of cloud platforms (AWS, GCP, Azure) and infrastructure as code (Terraform, CloudFormation).Familiarity with data engineering practices (ETL, data validation, schema management).Experience with model monitoring and observability tools (EvidentlyAI, Prometheus, Grafana).Understanding of distributed systems, container orchestration, and microservice design. Nice-to-Have SkillsExperience with LLMOps / GenAI deployment (LangChain, Hugging Face, Triton Inference Server).Exposure to data versioning (DVC, LakeFS) and feature stores (Feast, Tecton).Knowledge of CI/CD automation for ML pipelines (Argo Workflows, GitOps).Certifications in cloud (AWS/GCP/Azure) or MLOps. Soft SkillsExcellent communication and cross-functional collaboration skills.Strong analytical and problem-solving abilities.Ability to translate ML research into production systems.Proactive and adaptable in a fast-paced AI/ML environment. EducationBachelor’s or Master’s in Computer Science, Data Engineering, AI, or related field (or equivalent experience). CompensationContractor rate based on experience.