AI Platform Engineer (m/f/d) in Böblingen, Deutschland

Are you seeking answers and opportunities for your future? At our site in Böblingen you will find both as AI Platform Engineer (m/f/d) Advantest Europe GmbH Böblingen Vollzeit Home-Office Advantest - We enable tomorrowʻs technology. Market leader Hightech-High-tech semiconductors International impact IoT, 5G and Artificial Intelligence. Unthinkable without us. More than half of all the microchips produced worldwide first pass through our hands. As the global market leader of automated test systems in the semiconductor industry we help the world to realize the digital transformation, enable our customers to shape the future and offer you the exciting jobs intended for pioneers. Your Responsibilities Own the target operating model for the CIT AI platform, including governed exploration, model access, deployment patterns, operational ownership and handover between teams and external partners. Define reusable platform patterns and standards for LLM APIs, RAG components, evaluation pipelines, AI gateway integration and business application integration. Set technical direction and priorities for MLOps Engineer(s), review key build decisions and ensure implementation choices remain aligned with platform standards. Own the transition path from sandbox or PoC environments into production-ready architectures, including support model, lifecycle ownership and operational readiness criteria. Define cost transparency and usage visibility for AI platform consumption, including token, cost and usage reporting patterns. Coordinate and steer nearshore, system integration and cloud implementation partners while retaining internal accountability for platform outcomes. Own platform decisions, security assumptions, interface documentation, architecture decisions and handover requirements at governance level. Act as the primary contact for architecture, security, governance, data engineering, cloud platform and application teams on AI platform matters. Report platform roadmap, risks, decisions, adoption progress and production-readiness status to CIO-level and senior stakeholders. Your Qualifications 7+ years of experience in platform engineering, DevOps, cloud engineering, ML engineering or enterprise software operations, including technical leadership or architecture responsibility. Track record of moving workloads from experimentation into stable, governed production operations at enterprise scale. Experience setting technical direction for a small engineering team and/or steering external delivery partners while retaining internal accountability. Strong background in Python-based engineering, CI/CD, Git-based workflows and modern software delivery practices, with the ability to review technical designs and code-level decisions. Solid understanding of Docker, Kubernetes and cloud AI/ML services on Azure or AWS. Working knowledge of MLOps concepts such as model registries, evaluation pipelines, drift monitoring, retraining workflows and production observability. Understanding of enterprise security expectations, including identity, network isolation, secrets management, API access control and data protection implications. Ability to communicate technical trade-offs clearly to architects, managers and CIO-level stakeholders. Fluency in English, spoken and written. Our offer Flexibility Benefits Development Fitness Security Flexibility: Flexible and trust-based working hours, 30 vacation days + option for additional vacation days, mobile working, individual part-time models and programs for extended periods of absence Benefits: Attractive salary, share in Advantest´s success through our exceptionally appealing bonus program as well as numerous subsidies, discounts and offerings (e.g. bike leasing) Development: Structured onboarding programs and mentoring, development discussions, technical and soft skill trainings, language courses and knowledge sessions Fitness: Ergonomic working environment, sports and fitness options and events (e.g. Global Challenge) as well as health days Security: Attractive company pension scheme, comprehensive insurance coverage and support in emergency situations Insights into our work environment Would you like to know more about jobs at Advantest and about our unique team spirit? Our employees offer you their personal insights into our working environment. Would you like to take a closer look how our working environment in Boeblingen or Amerang looks like? Here you can take a short virtual 360° tour of our company - click the image to get to know us better. Contact If you have any questions, Ann-Kathrin Rupp will be happy to answer them. Advantest Ann-Kathrin Rupp Böblingen +49 (0) 7031.204.8022. Take your next career step with us! Apply now, preferably via our online application tool. Apply now For further information visit: Own the target operating model for the CIT AI platform, including governed exploration, model access, deployment patterns, operational ownership and handover between teams and external partners. Define reusable platform patterns and standards for LLM APIs, RAG components, evaluation pipelines, AI gateway integration and business application integration. Set technical direction and priorities for MLOps Engineer(s), review key build decisions and ensure implementation choices remain aligned with platform standards. Own the transition path from sandbox or PoC environments into production-ready architectures, including support model, lifecycle ownership and operational readiness criteria. Define cost transparency and usage visibility for AI platform consumption, including token, cost and usage reporting patterns. Coordinate and steer nearshore, system integration and cloud implementation partners while retaining internal accountability for platform outcomes. Own platform decisions, security assumptions, interface documentation, architecture decisions and handover requirements at governance level. Act as the primary contact for architecture, security, governance, data engineering, cloud platform and application teams on AI platform matters. Report platform roadmap, risks, decisions, adoption progress and production-readiness status to CIO-level and senior stakeholders. 7+ years of experience in platform engineering, DevOps, cloud engineering, ML engineering or enterprise software operations, including technical leadership or architecture responsibility. Track record of moving workloads from experimentation into stable, governed production operations at enterprise scale. Experience setting technical direction for a small engineering team and/or steering external delivery partners while retaining internal accountability. Strong background in Python-based engineering, CI/CD, Git-based workflows and modern software delivery practices, with the ability to review technical designs and code-level decisions. Solid understanding of Docker, Kubernetes and cloud AI/ML services on Azure or AWS. Working knowledge of MLOps concepts such as model registries, evaluation pipelines, drift monitoring, retraining workflows and production observability. Understanding of enterprise security expectations, including identity, network isolation, secrets management, API access control and data protection implications. Ability to communicate technical trade-offs clearly to architects, managers and CIO-level stakeholders. Fluency in English, spoken and written.