MLOps Engineer (m/f/d)

advantestcareers; Böblingen; Vor Ort; EUR 75.000 - 110.000; Vollzeit

Zusammenfassung

Advantest is seeking an experienced AI Platform Engineer to design and run CI/CD pipelines and production-grade ML/AI infrastructure. You will implement model registries, serving pipelines and AI gateway integrations for internal apps, while ensuring observability, cost control and reliability across cloud and container environments.

You will collaborate with data engineering, application teams and security to meet deployment, identity and access requirements, and you will advance runbooks and

Qualifikationen

• 3–5 years in DevOps, cloud engineering, ML/AI platform engineering, or related roles.
• Hands‑on with CI/CD, IaC and automated deployment in production.
• Strong Python skills and Git-based development workflows.
• Experience with Docker/Kubernetes; Helm is a plus.
• Experience with Azure or AWS cloud services.
• Familiarity with observability tools (Prometheus, Grafana) and logging/alerting.
• Understanding of MLOps concepts: model registries, evaluation pipelines, and model lifecycle.

Aufgaben

• Implement and operate CI/CD pipelines for AI/ML workloads.
• Build and maintain model registry and model serving for LLM APIs.
• Configure observability for model usage, cost, latency and reliability.
• Support transition of workloads from sandbox to production following runbooks.
• Develop reusable components for LLM API integration and RAG pipelines.
• Execute infrastructure-as-code across Docker, Kubernetes and Helm deployments.
• Maintain runbooks, docs and operational dashboards.
• Support incident analysis, reliability improvements and cost optimization.
• Collaborate with data engineering and security teams on integration and access requirements.

Kenntnisse

CI/CD

Python

Git workflows

Docker

Kubernetes

Cloud services (Azure/AWS)

Tools

Prometheus

Grafana

Helm

Terraform

Jobbeschreibung

Responsibilities

• 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.

Qualifications

• 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.