Thomson Reuters is looking to solve the problem of optimizing systems and services for security, automation, reliability, and performance/availability, while ensuring solutions adhere to architecture standards and organizational values, specifically in the context of AI-driven solutions and DevOps.
Requirements
- 8+ years of overall software engineering / DevOps / platform engineering experience, including 3+ years in a Lead‑level DevOps / Platform / SRE capacity, and 2+ years supporting AI‑driven solutions at enterprise scale.
- Strong experience designing and operating solutions on cloud platforms (Azure and/or AWS), including core services such as compute, storage, networking, identity, and managed databases.
- Hands‑on expertise with Kubernetes and containerization (Docker), including building and deploying containerized workloads at scale; experience with managed Kubernetes (e.g., AWS EKS and/or Azure AKS).
- Deep knowledge and hands‑on experience with CI/CD and MCPS tools, including at least two of: Azure DevOps (ADO), Jenkins, GitHub Actions, with a track record of planning, building, and deploying cloud‑based solutions.
- Experience implementing and supporting MCP server architectures, orchestration workflows, and agentic pipelines in production environments.
- Demonstrated experience with AI/ML Ops concepts and tooling (e.g., model/pipeline versioning, evaluation, monitoring, rollout/rollback strategies).
- Strong scripting and programming skills, preferably in Python, Bash, and/or PowerShell; ability to build automation, tools, and integrations.
Responsibilities
- Architect and implement AI‑driven solutions using agentic AI patterns, including MCP server architectures, orchestration workflows, and agentic pipelines.
- Design and operate scalable, secure, and cost‑efficient AI platforms on cloud infrastructure (Azure and/or AWS) with Kubernetes as the primary runtime.
- Integrate LLMs, vector search, and retrieval‑augmented generation (RAG) patterns using services such as Azure AI Foundry and Azure AI Search.
- Define and implement AI/ML Ops practices for model and pipeline lifecycle, including versioning, monitoring, evaluation, and governance.
- Plan, deploy, and maintain critical business applications and AI services in production and non‑production cloud environments (Azure / AWS).
- Design and implement appropriate environments for those applications and services; engineer robust release management procedures and provide production support.
- Build and maintain CI/CD pipelines using MCPS tooling (e.g., Azure DevOps, Jenkins, GitHub Actions), including automation for building, testing, scanning, and deploying AI and non‑AI workloads.
Other
- Strong communication and collaboration skills, with experience influencing across teams and mentoring other engineers.
- Ability to work in a flexible hybrid working environment (2-3 days a week in the office depending on the role).
- Experience working in regulated or compliance‑sensitive environments (e.g., legal, tax, financial services) with attention to data protection and governance.
- Bachelor's degree in Computer Science, Engineering, or related field.
- 8+ years of overall software engineering / DevOps / platform engineering experience.
