DevOps Engineer with AI/ML Evaluation Focus

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 & Enablement: Document model best practices, limitations, and use cases; track market developments; collaborate with business departments to identify new use cases; support compliance on data protection, security, and AI regulations, particularly the EU AI Act and GDPR (internal deployer-side use, not product conformity); train and enable internal staff on AI/ML tools, contributing to AI literacy under Art. 4 of the EU AI Act and internal AI governance/use guidelines Your Profile: Completed degree in Computer Science, IT, or comparable professional experience 2 to 4 years of experience in a DevOps environment Hands-on experience with CI/CD tooling; both GitLab CI and Jenkins are in use Experience with at least one major public cloud (AWS, Azure, or GCP); all three are in use Solid experience operating Linux systems (Ubuntu), as the large majority of systems run on it Experience with containerization (Docker); Kubernetes knowledge is a plus, as it is used on a per-project basis Solid working knowledge of the core DevOps toolchain: Infrastructure as Code (Terraform, Ansible), scripting (Python, Bash), and monitoring, logging, and alerting (e.g. Prometheus, Grafana, ELK Stack) Basic knowledge or hands-on experience working with AI/ML models or APIs (e.g., LLM providers) An analytical mindset for objectively evaluating tools and vendors Strong communication skills for collaborating with technical and non-technical teams Fluent in English, spoken and written What we offer A dynamic high-tech company, combined with financial soundness and world-class inve