Senior DevSecOps Engineer

We’re recruiting for a Senior DevSecOps / Data Platform Engineer to join a highly technical environment delivering secure platforms across complex public-sector programmes.

This is not a traditional DevOps role. The position sits across DevSecOps, platform engineering, data engineering and MLOps, with a strong focus on building secure, scalable environments that support data pipelines and machine-learning workloads.

You’ll be working with modern open-source technologies, containerised platforms and secure deployment environments, helping to design the infrastructure and pipelines that allow data and ML workloads to run reliably, including at the edge.

What you’ll be doing

• Designing and delivering secure CI/CD pipelines
• Building and supporting Kubernetes and container-based platforms
• Working with GitLab, Docker, Linux, Python, Bash and YAML
• Automating infrastructure, deployments and platform operations
• Building and improving data pipelines and platform services
• Supporting MLOps and machine-learning deployment
• Working with open-source technologies in secure and restricted environments
• Embedding security, governance and assurance into delivery processes
• Contributing to technical architecture and engineering standards

What we’re looking for

• Strong experience in DevSecOps
• Deep hands-on experience with Kubernetes
• Strong CI/CD experience, ideally with GitLab
• Good Linux and scripting capability using Python and/or Bash
• Experience with containerisation and secure software delivery
• Exposure to data platforms, data pipelines or MLOps
• Experience working with open-source technologies
• Background in government, defence, regulated or restricted environments would be highly beneficial
• Experience with air-gapped environments would be particularly useful

The hiring team is realistic that the perfect person may not have every skill listed. The priority is someone with genuine technical depth in DevSecOps, Kubernetes and secure platform engineering, with enough data and ML exposure to operate comfortably in a data-platform environment.