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
If you have any questions, Ann-Kathrin Rupp will be happy to answer them.
AdvantestAnn-Kathrin Rupp
Böblingen
+49 (0) 7031.204.8022
For further information visit: www.advantest-career.de