Machine Learning Operations Engineer til opbygning af Forsvarets nye IT-platform

This role as a Machine Learning Operations (MLOps) Engineer involves building and maintaining IT platforms specifically for defense sector applications. Key competencies include expertise in machine learning lifecycle management, encompassing model deployment, monitoring, and continuous integration/continuous deployment (CI/CD) pipelines tailored for ML systems. Proficiency with cloud platforms (such as AWS, Azure, or Google Cloud) and container orchestration tools like Kubernetes and Docker is essential to ensure scalable, reliable infrastructure. Familiarity with infrastructure-as-code tools (e.g., Terraform, Ansible) supports automated and repeatable environment setups. Strong programming skills in languages commonly used in ML workflows, such as Python, are critical, along with experience in ML frameworks (e.g., TensorFlow, PyTorch) to collaborate effectively with data scientists. Knowledge of data engineering principles, including data pipelines and storage solutions, is valuable for managing input data for models. Given the defense context, understanding security best practices, compliance standards, and the ability to work within stringent regulatory environments is important. Collaboration skills are necessary to work closely with cross-functional teams including software engineers, data scientists, and IT operations. While specific experience and education levels are not detailed, the role likely requires a solid foundation in computer science or related fields, combined with practical experience in MLOps or DevOps applied to machine learning projects. Overall, the position demands a blend of software engineering, machine learning, and operational expertise to support the development and deployment of advanced IT platforms for defense purposes.