DevSecOps Engineer — AI Platform
Roles & Responsibilities
2 days WFH
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
Tell employers what skills you have
Infrastructure Monitoring
AI Agents
hands-on skills
Pipeline Management
Case Management Systems
Business Ownership
Software Quality Engineering
Structured Analysis
Systems Management
Selling security solutions
resolve system issues
Confidence Building
Escalation Process
Prompt Engineering
Change Management Process
DevSecOps