DevOps Engineer

SR2 Consulting is looking for a DevOps Engineer to establish and improve that delivery lifecycle within a financial-services organisation developing its internal AI capability.

Working alongside AI and platform engineers, you’ll help teams release changes safely, identify problems quickly and operate services with clear accountability.

What you’ll be doing

• Build and maintain CI/CD pipelines for AI applications, APIs and supporting services.
• Automate testing, code-quality checks, dependency scanning and security controls within the delivery process.
• Establish consistent approaches to build artefacts, environment promotion, release approvals and rollback.
• Work with AI engineers to incorporate application evaluations into release gates, including changes to prompts, model configuration and retrieval components.
• Improve logging, metrics, tracing and alerting so teams can understand service behaviour and diagnose failures.
• Support incident investigation, root-cause analysis and improvements to operational readiness.
• Automate repetitive delivery and support tasks, reducing reliance on manual intervention.
• Maintain runbooks and release evidence appropriate to a regulated environment.

• Proven DevOps experience supporting production software delivery.
• Experience in a regulated industry is essential, ideally banking, insurance or wider financial services.
• Strong CI/CD engineering skills and practical experience with cloud-hosted applications.
• Confidence with scripting, source control, containerisation and infrastructure as code.
• An understanding of secure software delivery, controlled change and auditable release processes.
• Experience with monitoring, incident response and production troubleshooting.
• The ability to work closely with developers and security teams, turning requirements into effective automation.

Relevant technology experience

Git, Python or Bash, Docker and Terraform are particularly useful, alongside a CI/CD platform such as GitHub Actions, Azure DevOps or GitLab CI.

Experience with AWS or Azure is relevant, as is familiarity with cloud monitoring services, OpenTelemetry, Prometheus or Grafana. Exposure to security tooling such as SonarQube, Trivy or equivalent tools would be beneficial.

Experience supporting AI/ML applications, model or experiment tracking, automated evaluations and versioning of AI application components is a strong advantage. MLflow or comparable tooling would be useful, but the priority is robust delivery engineering rather than a particular product.