The posting
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



