DevOps Engineer with AI/ML Evaluation Focus

<h4>About the Position:</h4> <p>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.</p> <h4>Responsibilities :</h4> <p></p> <ul> <li><strong>CI/CD, Automation &amp; Software Development:</strong> 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.</li> <li><strong>Cloud, Infrastructure &amp; Operations:</strong> 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.</li> <li><strong>Production Support &amp; AI/ML Evaluation:</strong> 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.</li> <li><strong>AI/ML Testing, Integration &amp; Support:</strong> 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.</li> <li><strong>AI Governance, Compliance &amp; Enablement:</strong> 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, p