Manager, Enterprise AI Services Engineering

Replacement: This position is open due to an existing vacancy to support our evolving business needs. Thomson Reuters is seeking a Manager, Enterprise AI Services Engineering. This role is responsible for leading the team that builds, operates, and evolves Thomson Reuters' Enterprise AI Services (EAIS) platform. This role owns the engineering delivery of the TR AI Gateway service - TR's central gateway through which product teams access large language models across Azure, AWS, and GCP. The successful candidate will lead a globally distributed team of full-time engineers and contractors across AMERS and APAC regions, drive quarterly engineering delivery against ADO sprint plans, and partner closely with Service Architecture, Enablement, Product Management, and Platform Engineering leadership to keep EAIS secure, scalable, and strategically aligned with TR's AI ambitions. About the Role As a Manager, Enterprise AI Services Engineering , you will: Lead and manage a globally distributed team of full-time engineers and contractors Own the quarterly engineering planning cycle using Azure DevOps Boards, running bi- weekly SCRUM ceremonies and maintaining sprint velocity and team health Drive deployment, implementation, and engineering of AI systems, tools, and pipelines across Azure, AWS, and GCP environments Ensure reliable, production-grade operations of the TR AI Gateway Service and other AI services with a strong focus on SLOs and incident management Maintain and improve CI/CD automation pipelines and infrastructure-as-code practices (cloud-iac) across multi-cloud environments Serve as the engineering bridge between EAIS Architecture (design and roadmap) and Service Engineering (build and operate), translating architectural decisions into executable delivery plans Partner with the Enablement team to support customer onboarding, intake management, and LLM consumer success across TR product teams Contribute to Core Platform Services OKR planning and quarterly reviews, owning EAIS engineering objectives Represent the team in Platform Engineering leadership forums, providing delivery, risk, and roadmap visibility to Core Platform Services and Platform Engineering leadership Ensure compliance with TR's AI Security Guidelines, FedRAMP requirements, and enterprise identity and access management standards (SailPoint, PingID, CyberArk, Venafi) Drive security hygiene through Snyk AppSec, certificate management, and GitHub signed-commit practices Manage Datadog observability across production and non-production environments, ensuring dashboards, alerting, and incident response are production-ready Support career development and performance management for direct reports Manage contractor relationships and hold vendors accountable to delivery standard About you You are a potential for for the role, Manager, Enterprise AI Services Engineering , if your background includes: 7 years of software or cloud engineering experience, with 3 years in an engineering management or technical lead role Deep hands-on experience with multi-cloud platforms: Azure (required), AWS (required), GCP (preferred Proven experience managing enterprise AI, ML, or LLM platform engineering teams in production environments Strong foundation in DevOps and CI/CD practices, including infrastructure-as-code, GitOps, and container orchestration (Kubernetes) Experience with agile delivery frameworks (Scrum/Kanban) using Azure DevOps Boards or equivalent tooling Familiarity with observability platforms (Datadog) and enterprise ITSM tools (ServiceNow) Understanding of enterprise identity and access management, cloud security, and compliance requirements Demonstrated ability to manage globally distributed, blended teams across time zones and cultures Collaborative approach to working across Architecture, Enablement, Product, and Cloud Engineering teams Experience with LLM orchestration layers, retrieval-augmented generation (RAG) pipelines, or enter ... (truncated, view full listing at source)