DevSecOps Engineer — AI Platform

About the role

You will be the main hands-on engineer across the full lifecycle of an AI-powered document processing and case intelligence platform on AWS GCC. This spans development and platform engineering; it is not a backend-only role. You will work with the business owner, business analyst and solution architect to translate user stories into technical requirements, and take ownership of end-to-end infrastructure and security solutions across the products and relevant systems. 12-month contract with an optional 12-month extension.

Key responsibilities

• Work with the business owner, business analyst and solution architect to translate user stories into technical requirements
• Develop and maintain API integrations with the case management system and any other required systems
• Design and build the agentic AI pipeline, including LLM API integration, prompt engineering, skill and context design, structured output schemas, confidence scoring and error handling
• Work with the Software Quality Engineer to develop automation and processes to deploy, manage, scale and monitor applications in data centre and cloud environments
• Troubleshoot and resolve system and application issues, participating in on-call escalations for critical incidents
• Take ownership of end-to-end infrastructure and security solutions across the products and relevant systems
• Deploy and manage monitoring tools to track infrastructure performance, utilisation and health
• Configure and maintain CI/CD pipelines, incorporating streamlined change management and release processes
• Develop scripts and automation tools to support software build, integration and deployment across development and production environments
• Plan, implement and monitor system security architecture, including threat and risk assessments

About you

• Minimum 4 years' relevant working experience
• Degree or diploma in Computer Science, Computer or Electronics Engineering, IT or a related discipline
• Passion for automation, standardisation and best practices in infrastructure and security
• Strong understanding of the Software Development Life Cycle (SDLC), Test-Driven Development (TDD), Continuous Integration (CI) and Continuous Delivery (CD)
• Track record in agentic AI system design: multi-agent pipelines, Bedrock AgentCore, orchestration and MCP Server, RAG knowledge bases, human-in-the-loop architecture
• Experience working with high-availability, high-performance and high-security multi-data-centre systems and hybrid cloud environments
• Proficiency in PowerShell and Python
• Experience with Git and modern branching workflows
• Strong understanding of container technologies (Docker, Kubernetes)
• Experience with CI/CD pipelines (GitHub Actions, GitLab CI)

Significant advantage

• Experience in regulatory or compliance contexts where agent outputs must be accurate, auditable and structured for expert reviewers

Added advantage

• Security certifications such as CREST, CISSP, CISM or relevant cloud security credentials

• Experience working in an organisation that successfully implemented a DevSecOps transformation

• Hands-on experience with API security, secrets management and zero-trust architectures

• Experience developing and deploying systems on GCC