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VP, AI Platform Engineering

Thomson Reuters is investing in AI as a core capability across Legal, Tax, Risk, News, and Corporates. We are seeking a senior Technology Leader to define and drive the technical direction for how enterprise AI systemsincluding multi-agent architectures, fine-tuned models, and retrieval-augmented solutionsare designed, governed, and scaled across the platform organization, while ensuring product engineering teams can safely and efficiently consume these capabilities.

This role leads a global AI Platform Engineering organization responsible for delivering AI Engineering Services. Positioned within Platform Engineering, it operates in close partnership with AI Labs, Product Engineering, Information Security, Legal, and Procurement to establish production-grade AI platforms and standards.

You will serve as the technical authority across three integrated domains: Enterprise AI Engineering Services, AI-Native Developer Enablement, and AI Platform Integrationensuring cohesive, secure, and scalable adoption of AI capabilities across Thomson Reuters. This includes leading a diverse global team of 35+ engineers and driving engineering rigor, platform standardization, and responsible AI practices.

As Vice President, AI Platform Engineering, you will report to the Head of Platform Engineering and be a key member of a high-performing organization at the center of Thomson Reuters' technology and AI strategy. You will own:

AI-Native Engineering and Developer Enablement

You will define what AI-native development means at Thomson Reuters: the agreed coding-agent stack, the prompt and evaluation standards, the CI/CD integrations, and the guardrails that make agent-assisted development safe inside the enterprise. You will govern the AI developer tooling estate (coding agents, prototyping tools, evaluation and observability platforms) and set patterns for MCP servers and developer-facing agents.

AI Platform and Tooling Integration

You will own the technical direction for the AI platform capabilities engineering teams across TR consume: model serving, evaluation infrastructure, RAG and retrieval infrastructure, fine-tuning workflows, prompt and observability tooling, and the integration of these capabilities with the existing platform estate (IDP, API management, observability, GitHub). You will partner with and align the Cloud Infrastructure agentic platform team to keep developer-facing and ops-facing AI strategies coherent.

Performance & Scalability

You will own the quality bar for security, scalability, and reliability of AI Platform Systems. Define and enforce observability and performance standards for AI-driven workloads operating at enterprise scale across millions of documents and interactions.

Collaboration & Leadership

You will lead and grow teams of AI engineers building the services and platforms that translate AI capabilities into real-world product applications. Foster a culture of experimentation, continuous improvement, and engineering excellence. Own hiring, performance, and mentorship across the organization.

Key responsibilities and impact

• Define, publish, and evolve TR's AI-native engineering standards, including reference implementations; support adoption through office hours and direct engagement with product engineering teams.
• Partner with InfoSec, Legal, Privacy, and the AI Council to ensure AI engineering practices are auditable, compliant, and production ready.
• Lead architecture reviews for AI systems entering the production estate, driving standards compliance and shaping the Platform Engineering portfolio ahead of demand.
• Lead technical evaluations and build-vs-buy decisions for AI infrastructure, model serving, evaluation platforms, and developer tooling.
• Represent Platform Engineering in M&A technical due diligence, where AI, cloud, and modern engineering posture are in scope.
• Drive broader engineering productivity and developer experience initiatives where AI intersects with IDP, DORA, API-first delivery, and Consumer Success engagements.
• Coach and mentor principal and staff engineers across the organisation, raising the bar for how TR designs, evaluates, and delivers production-grade AI systems.
• Own the publication of TR's AI-native reference implementations and drive adoption across product engineering teams.
• Represent Thomson Reuters externally at AI engineering, platform, and architecture forums to both learn from and influence industry best practices.

Location

This hybrid role can be based in any of the following hub locations: Eagan, MN; Frisco, TX or New York, NY.

About You

You are a strong fit for the VP, AI Platform Engineering role if you possess the following skills and experience:

• 15+ years building and operating software at enterprise scale, with at least 5 years in AI/ML engineering with a focus on production deployment.
• Proven track record building and shipping multi-agent AI systems at enterprise scale.
• Deep expertise in LLM fine-tuning techniques (DPO, RAG, multi-turn prompting).
• Experience designing and implementing agentic AI architectures: tool-use, orchestration, reasoning, evaluation.
• Strong background in MLOps and ML data infrastructure (Spark, Kafka, Kubernetes, model serving, feature stores).
• Working fluency across the current AI-developer-tooling landscape: coding agents, MCP, evaluation frameworks, prompt and eval observability. You should be using this stuff yourself, not just reading about it.
• Experience with cloud AI services across AWS (Bedrock, SageMaker), Azure, GCP, or OCI.
• Proficiency in Python and modern ML frameworks (PyTorch, HuggingFace, LangChain, AutoGen, vLLM).
• Deep engineering credibility with hands-on AI engineering experience. You carry the technical depth to lead architecture reviews, set the standard, and drive engineering quality across teams.
• Familiarity with engineering productivity frameworks (DORA, SPACE, DX) and the limits of each.
• Comfort operating across CTO and CIO governance, product engineering leaders, security, legal, and procurement. You know how to land AI strategy in a regulated, multi-segment business.
• Strong written communication. You will be writing standards, not just slide decks.
• Demonstrated ability to lead research teams and translate academic work into production systems.
• Track record of organizational leadership and scaling cross-functional teams.

Nice to have

• Graduate degree in Computer Science, AI, Data Science, or related field.
• Active academic researcher or open-source contributor in agentic AI, LLM customisation, or related areas.
• Experience with conversational AI, document intelligence (LayoutLM-style models), or domain-specific dialogue systems.
• Prior experience in leading teams of engineers, AI researchers, and product development teams to translate AI capabilities into real-world applications, owning large projects or workstreams.
• Experience in legal, tax, financial services, or other regulated information businesses.
• Experience defining and shipping data catalogues and workflows at scale (Port, Backstage, or comparable).

What Success Looks Like In The First 12 Months

• TR has a published AI engineering standards document and reference architecture for multi-agent systems, adopted by at least three product or platform teams.
• A defended set of evaluation, observability, and production-readiness criteria exists for AI systems entering the TR production estate.
• The AI developer tooling estate is rationalised, governed, and procured under enterprise terms rather than team-by-team purchase.
• TR's AI-native development standard is published, with measurable adoption across the top 12 master products.
• TR's AI-native reference implementations are published and adopted across product engineering teams.
• Production quality of AI systems has improved, reflected in fewer architectural exceptions, stronger auditability, and faster time-to-approval for compliant systems.