AI Platform Engineer (m/f/d)

advantestcareers; Böblingen; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

Advantest is seeking an experienced Platform Engineer to own the CIT AI platform operating model and drive production-ready architectures. You will define patterns for LLM APIs, RAG components, and evaluation pipelines, while leading MLOps engineers and coordinating with cloud partners.

The role emphasizes governance, security, and reporting to CIO-level stakeholders, with a focus on enterprise-scale delivery and measurable platform adoption.

Qualifikationen

• Extensive experience in platform and enterprise-grade engineering.
• Proven ability to drive production-ready architectures from PoC.
• Strong leadership and collaboration with internal and external teams.

Aufgaben

• Own target operating model for AI platform including governance and handover.
• Define reusable patterns for LLM APIs, RAG, evaluation pipelines and integration.
• Set technical direction for MLOps engineers and review key build decisions.
• Lead transition from sandbox to production, ensuring operational readiness.
• Define cost transparency and usage reporting for platform consumption.
• Coordinate with nearshore and cloud partners while maintaining accountability.

Kenntnisse

Platform engineering

DevOps

Cloud engineering

ML engineering

Python

CI/CD

Git workflows

Production observability

Tools

Docker

Kubernetes

Azure

AWS

Jobbeschreibung

• 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.