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Engagement: DevOps, AI and Automation Engineer
Engagement Type: Outside IR35 Contract
Duration: 6 months
Why Tokio Marine HCC?
Standing still is not an option in the current world of Insurance. TMHCC are one of the world’s leading Specialty Insurers. With deep expertise in our chosen lines of business, our unparalleled track record and a solid balance sheet, TMHCC evaluates and manages risk like no one else in the industry. Looking beyond profit, empowering our people and delivering on our commitments are at the core of our customer values, and so is a desire to grow and provide creative and innovative solutions to our clients.
This assignment is to deliver a defined AI-enabled DevOps and automation capability for TMHCC, including CI/CD patterns, platform automation, operational tooling, Infrastructure-as-Code standards, documentation, and deliver a structured knowledge transfer, including documentation, to support scalable adoption of AI-enabled engineering practices.
Deliverables
- Deliver scalable CI/CD patterns and deployment processes for AI-enabled platforms and services.
- Integrate Azure AI Foundry and approved AI services into enterprise deployment workflows and operational processes.
- Identify tooling, controls, deployment patterns, and operational requirements required to integrate AI services into enterprise CI/CD pipelines.
- Provide structured knowledge transfer and operational handover of AI deployments to TMHCCI DevOps and Engineering teams.
- Deliver agreed outcomes in line with defined milestones, providing concise progress reporting against deliverables, risks, and key decisions.
- Identify and contribute to delivering targeted improvements to the SDLC leveraging AI capabilities, with demonstrable impact on efficiency, quality, or speed of delivery
Technologies
- CI/CD & DevSecOps: Azure DevOps, GitHub Actions, CheckMarx, JFrog Artifactory, Octopus Deploy
- Infrastructure & Automation: Terraform, Ansible
- Containers & Platforms: Docker, Kubernetes, Rancher, Azure, Windows, Linux
- Observability: Datadog, Grafana, Prometheus, OpenTelemetry
- AI Enablement: Azure AI Foundry, Model Context Protocol (MCP)
Expected Outcomes
- Establish scalable and secure CI/CD processes for AI-enabled platforms and services.
- Improve automation, deployment consistency, and operational reliability.
- Deliver reusable engineering standards and Infrastructure-as-Code patterns.
- Improve observability, operational visibility, and deployment governance.
- Enable long-term adoption through documentation, structured knowledge transfer, and operational maturity across TMHCC engineering teams.
