Cloud DevOps and MLOps Engineer

About the role

This role supports the technical environments of the WDT and R&D projects. The work includes development, acceptance, pilot, observability, CI/CD, model serving, access control, cloud infrastructure, operational security and support for AI, simulation and data workloads.

The person will connect software, cloud, AI and operations. The aim is for the WDT and its modules to be developed, tested, deployed, monitored and demonstrated reliably in representative environments.

Main responsibilities

• Design and maintain development, test, acceptance, pilot and production environments for WDT modules.
• Implement CI/CD pipelines, deployment automation, configuration control and release versioning.
• Operate cloud infrastructure, storage, databases, queues, backend services, observability and logs.
• Support MLOps, model serving, benchmarks, controlled inference and monitoring of AI resources.
• Work on operational security, environment segregation, access control and incident response.
• Support tests of performance, resilience, availability, cost and technical continuity.
• Document environments, runbooks, indicators and assisted-operation procedures.

Required qualifications

• Experience with cloud, CI/CD, infrastructure as code or environment automation.
• Knowledge of Git, build and release pipelines, monitoring, logs and deployment practice.
• Familiarity with networks, authentication, permissions, containers or managed services.
• Ability to diagnose failures in distributed systems and support development teams.
• Attention to security, stability, traceability and operational documentation.
• Practical experience with development, test and acceptance environments.

Preferred qualifications

• Experience with Azure, Azure DevOps, App Services, Functions, Service Bus, VMs, Blob Storage or Cosmos DB.
• Knowledge of Docker, Kubernetes, Terraform, observability and cost control.
• Background in MLOps, model serving, vector databases, LLM APIs and AI workloads.
• Experience with engineering systems, digital twins, simulations or sensitive technical data.

Related technologies and tools

Azure, Azure DevOps, CI/CD, Git, Docker/Kubernetes as a plus, observability, logs, storage, databases and MLOps.

Profile

We are looking for an organized, reliable, operations-minded person. They should enjoy creating predictable environments, automating repetition and giving real visibility into the state of systems.

How to apply

To apply, send the following materials:

How do you currently use artificial-intelligence tools in your professional workflow? Name the tools, give examples of activities, and explain how you validate the results produced with AI assistance.

Additional information may include LinkedIn, GitHub, Behance, Google Scholar, articles, publications, websites, demonstrations, applications or other links relevant to the role.

• An up-to-date curriculum vitae with education, professional experience, main projects, technologies used and responsibilities held.
• A portfolio with concrete examples of previous work. For software, this may be GitHub, applications, systems, APIs, interfaces or projects that can be demonstrated. For engineering, this may be models, simulations, reports, analyses, publications or technical work that can be shared. Confidential projects may be presented in anonymized form.
• A presentation video of up to 5 minutes, briefly showing a project or portfolio, the problem solved, your contribution, the technologies used, your working workflow, everyday tools, your use of artificial intelligence and how you validate results produced with AI support.