DevOps Engineer with AI/ML Evaluation Focus

Job description

About the Position: We are looking for an experienced, motivated mid-level professional for a dual role: classic IT DevOps responsibilities combined with the evaluation and support of AI/ML models and vendors. This position is a great fit for someone who wants to combine technical infrastructure expertise with an interest in current AI/ML developments. The role sits in internal IT and is not part of robotics product development. The two areas are not equally plannable: production operations take precedence, and AI/ML evaluation runs as project work around operational load. Responsibilities : CI/CD, Automation & Software Development: Build, maintain, and improve CI/CD pipelines using GitLab CI and Jenkins; automate recurring operational processes; collaborate with development teams on deployment processes; develop small-scale internal software for IT and other internal departments; support software integrations, particularly Entra ID, Active Directory, and SSO. Cloud, Infrastructure & Operations: Manage and optimize AWS, Azure, and GCP infrastructure; use Infrastructure as Code (Terraform, Ansible), Docker and Kubernetes on select projects (not a global standard); operate the largely Linux-based landscape (approx. 80–85% Ubuntu), including patch/update management; ensure availability, scalability, security, monitoring, logging and alerting (Prometheus, Grafana, ELK); manage backup/disaster recovery and cloud cost optimization. Production Support & AI/ML Evaluation: Manage incidents and troubleshoot production environments (no on-call rota currently planned; if introduced, it will be agreed and compensated separately); evaluate AI/ML models and vendors (e.g. OpenAI, Anthropic, Google, Microsoft Azure AI) for performance, cost, suitability, cost-benefit, and licensing; evaluate open-weight/self-hosted models (e.g. vLLM, Ollama) where data residency, data protection, or cost requirements make external APIs unsuitable. AI/ML Testing, Integration & Support: Define objective AI/ML test criteria and benchmarks with the team lead; build and maintain evaluation datasets and re-run them after new model versions to detect regressions; support integration into existing systems/workflows, including RAG and agent tooling; act as first point of contact for technical AI/ML issues, providing first-level support and escalating to the team lead. AI Governance, Compliance & Ena…