{"id":481162,"title":"MLOps Engineer (m/f/d)","company":"Advantest Europe GmbH","city":"Böblingen","region":"Baden-Württemberg","location":"Böblingen, Baden-Württemberg","work_model":"remote","employment_type":"full_time","employment":["full_time"],"roles":["devops"],"visa":"not_mentioned","relocation":"not_mentioned","posted_at":"2026-09-10T12:35:14+00:00","tools":["MLOps","CI/CD","Infrastructure as Code","Python","Git","Docker","Kubernetes","Helm"],"seniority":"mid","years_experience_min":3,"german_requirement":"not_mentioned","german_level":"unspecified","english_requirement":"required","english_level":"unspecified","team_language":"unknown","confidence":0.4,"method":"rules","language_badge":"English ad, no German stated","badge_key":"english_ad","language_evidence":null,"requirements_snippet":"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 …","url":"https://www.jobware.de/job/mlops-engineer-m-f-d-064103247","source":"careersite","agency":null,"description_text":"MLOps Engineer (m/f/d)\nFirma: Advantest Europe GmbH\nEinsatzort: Böblingen\nHomeoffice Option, Vollzeit\n\nAufgaben:Implement and operate CI/CD pipelines, automated testing and release processes for AI/ML workloads.\nBuild and maintain model registry, model serving and AI gateway integrations for LLM APIs and internal applications.\nConfigure and maintain observability for model usage, cost, token consumption, latency, reliability and quality signals using tools such as Prometheus, Grafana, logging and alerting platforms.\nSupport the transition of workloads from sandbox or PoC environments into production by following defined standards, runbooks and support models.\nImplement reusable technical components for LLM API integration, RAG pipelines, evaluation pipelines and integration with business applications.\nExecute infrastructure-as-code for platform environments across container and cloud infrastructure, including Docker, Kubernetes and Helm-based deployment patterns.\nMaintain runbooks, operating procedures, technical documentation and operational dashboards for platform components.\nSupport incident analysis, reliability improvements, cost optimization and lifecycle maintenance for production AI workloads.\nWork with nearshore, system integration or cloud partners on specific implementation tasks as directed by the AI Platform Engineer.\nCollaborate with data engineering, application development, cloud platform and security teams on integration, identity, access and deployment requirements.\nQualifikationen:3-5 years of experience in DevOps, cloud engineering, ML engineering, MLOps or platform engineering.\nHands-on experience with CI/CD, infrastructure as code and automated deployment in production environments.\nStrong practical Python skills and Git-based development workflows.\nExperience with Docker and Kubernetes; deployment tooling such as Helm is desirable.\nWorking experience with Azure or AWS cloud services, including compute, storage and IAM concepts.\nExperience with observability tooling such as Prometheus, Grafana, logging platforms and alerting practices.\nFamiliarity with MLOps concepts such as model registries, evaluation pipelines, drift monitoring and model lifecycle management.\nUnderstanding of network isolation, identity, secrets management and API access control.\nFluency in English, spoken and written.","responsibilities_text":"Implement and operate CI/CD pipelines, automated testing and release processes for AI/ML workloads.\nBuild and maintain model registry, model serving and AI gateway integrations for LLM APIs and internal applications.\nConfigure and maintain observability for model usage, cost, token consumption, latency, reliability and quality signals using tools such as Prometheus, Grafana, logging and alerting platforms.\nSupport the transition of workloads from sandbox or PoC environments into production by following defined standards, runbooks and support models.\nImplement reusable technical components for LLM API integration, RAG pipelines, evaluation pipelines and integration with business applications.\nExecute infrastructure-as-code for platform environments across container and cloud infrastructure, including Docker, Kubernetes and Helm-based deployment patterns.\nMaintain runbooks, operating procedures, technical documentation and operational dashboards for platform components.\nSupport incident analysis, reliability improvements, cost optimization and lifecycle maintenance for production AI workloads.\nWork with nearshore, system integration or cloud partners on specific implementation tasks as directed by the AI Platform Engineer.\nCollaborate with data engineering, application development, cloud platform and security teams on integration, identity, access and deployment requirements.","requirements_text":"3-5 years of experience in DevOps, cloud engineering, ML engineering, MLOps or platform engineering.\nHands-on experience with CI/CD, infrastructure as code and automated deployment in production environments.\nStrong practical Python skills and Git-based development workflows.\nExperience with Docker and Kubernetes; deployment tooling such as Helm is desirable.\nWorking experience with Azure or AWS cloud services, including compute, storage and IAM concepts.\nExperience with observability tooling such as Prometheus, Grafana, logging platforms and alerting practices.\nFamiliarity with MLOps concepts such as model registries, evaluation pipelines, drift monitoring and model lifecycle management.\nUnderstanding of network isolation, identity, secrets management and API access control.\nFluency in English, spoken and written.","status":"open","duplicate_of":null,"first_seen":"2026-11-11T04:56:37.272879+00:00","responsibilities_summary":null,"requirements_summary":null,"all_tools":["MLOps","CI/CD","Infrastructure as Code","Python","Git","Docker","Kubernetes","Helm","Azure","AWS","IAM","Prometheus","Grafana","LLM","RAG"],"posting_language":"en","evidence":[{"language":"english","requirement":"required","quote":"Fluency in English, spoken and written.","source":"rules"}],"years_experience_max":5,"mobility_evidence":[],"model_version":"rules-10","english_badge":"English required","company_id":null}