DevSecOps Architect - Director

Job summary

The DevSecOps Architect owns the architecture for building, testing, securing, and releasing software across an enterprise engineering estate, while extending lifecycle controls to AI-assisted development and production AI/ML systems. The role standardises CI/CD and test automation, embeds security controls and audit evidence into delivery pipelines, and defines AI governance from evaluation through deployment and monitoring. Relevant technologies include Azure DevOps, Kubernetes, Terraform, and MLOps/LLMOps tooling.

Benefits

Qualifications

• Significant experience architecting enterprise-scale CI/CD and DevSecOps platforms in regulated environments
• Demonstrated delivery of test automation strategies that improved release frequency or defect escape rates
• Practical pipeline security integration experience, including supply chain controls
• Hands-on experience with production AI/ML or LLM systems, including evaluation, deployment, and monitoring

Responsibilities

• Define CI/CD architecture for branching, builds, artefact management, environment promotion, releases, and rollback
• Converge fragmented toolchains on a supported paved-road platform and establish a legacy pipeline deprecation path
• Automate deployment and change processes to meet audit and change-management requirements
• Own test automation across unit, integration, contract, end-to-end, performance, and resilience testing
• Define test data management, quality gates, and release reporting
• Embed SAST, DAST, SCA, container and infrastructure-as-code scanning, secrets detection, and SBOM generation in pipelines
• Define software supply chain controls for artefact provenance, signing, dependencies, and base images
• Establish policy-as-code and automated compliance evidence
• Define guardrails for AI-assisted engineering and controls for AI-generated code
• Define AI/ML delivery lifecycles, including data lineage, model versioning, evaluation, deployment, monitoring, and rollback
• Establish testing and control frameworks for AI systems, including model changes, explainability, human oversight, and drift monitoring
• Define reference architecture for AI platform components, including model gateways, retrieval, guardrails, observability, and audit trails

Skills

Work schedule

Industry

Relocation