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MLOps Engineer (m/f/d)

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ArbeitsortBöblingen
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Böblingen
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  • Implement and operate CI/CD pipelines, automated testing and release processes for AI/ML workloads.
  • Build and maintain model registry, model serving and AI gateway integrations for LLM APIs and internal applications.
  • Configure and maintain observability for model usage, cost, token consumption, latency, reliability and quality signals using tools such as Prometheus, Grafana, logging and alerting platforms.
  • Support the transition of workloads from sandbox or PoC environments into production by following defined standards, runbooks and support models.
  • Implement reusable technical components for LLM API integration, RAG pipelines, evaluation pipelines and integration with business applications.
  • Execute infrastructure-as-code for platform environments across container and cloud infrastructure, including Docker, Kubernetes and Helm-based deployment patterns.
  • Maintain runbooks, operating procedures, technical documentation and operational dashboards for platform components.
  • Support incident analysis, reliability improvements, cost optimization and lifecycle maintenance for production AI workloads.
  • Work with nearshore, system integration or cloud partners on specific implementation tasks as directed by the AI Platform Engineer.
  • Collaborate with data engineering, application development, cloud platform and security teams on integration, identity, access and deployment requirements.
  • 3-5 years of experience in DevOps, cloud engineering, ML engineering, MLOps or platform engineering.
  • Hands-on experience with CI/CD, infrastructure as code and automated deployment in production environments.
  • Strong practical Python skills and Git-based development workflows.
  • Experience with Docker and Kubernetes; deployment tooling such as Helm is desirable.
  • Working experience with Azure or AWS cloud services, including compute, storage and IAM concepts.
  • Experience with observability tooling such as Prometheus, Grafana, logging platforms and alerting practices.
  • Familiarity with MLOps concepts such as model registries, evaluation pipelines, drift monitoring and model lifecycle management.
  • Understanding of network isolation, identity, secrets management and API access control.
  • Fluency in English, spoken and written.

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