Lead AI Forward Engineer at Thomson Reuters in Eagan, MN

Job description

Job Description The Lead AI Forward Engineer designs and guides the delivery of AI-powered solutions that reduce operational toil and accelerate technology teams across the CIO organization. This role operates as a forward-deployed solution architect and engineer, partnering closely with teams to identify opportunities, design end-to-end architectures, and drive implementations to production. You will own solution design from concept through deployment, ensuring solutions are scalable, maintainable, extensible, secure, and operationally reliable. You will evaluate emerging AI technologies, define repeatable patterns, and help build new capabilities through hands-on implementation, mentorship, and shared standards. Key Responsibilities Identify high-impact opportunities to apply AI automation and intelligent agents across CIO technology teams. Partner with engineering teams, service owners, and stakeholders to translate business needs into technical requirements, solution designs, and delivery plans. Design end-to-end AI solutions, including workflows, integration patterns, data flows, APIs, and operational considerations. Guide implementations from prototype through production, ensuring solutions meet reliability, security, compliance, and maintainability expectations. Define reusable architectural patterns and reference designs to enable broader adoption of AI capabilities across teams. Build scalable pipelines to collect and analyze inference-level and workflow-level telemetry, integrating data with Thomson Reuters' data backbone. Develop dashboards and reporting that provide visibility into AI performance, reliability, safety, usage, and cost. Ensure compliance with Thomson Reuters AI standards for monitoring, governance, privacy, auditability, and operational controls. Evaluate and recommend AI/ML technologies and platforms—including LLM orchestration, agentic frameworks, cloud AI services, and observability tooling—based on capability, cost, risk, and enterprise fit. Design flexible architectures that can adapt to changing models, providers, technical requirements, and emerging AI capabilities. Apply sound judgment on when AI is appropriate and when simpler automation or traditional engineering approaches are better suited to the problem. Establish and track SLIs and SLOs for critical AI services to meet enterprise reliability, performance, and compliance requirements. Integrate AI observability tooling into CI/CD processes so new models, prompts, workflows, and use cases are automatically enrolled in monitoring and evaluation. Develop automated guardrails and policy-enforcement mechanisms, such as limits, anomaly detection, and abuse or failure-pattern detection, in partnership with cloud engineering and security teams. Partner with Product, Data Science, AI Inference Engineering, and Enterprise AI teams to design and operate evaluation frameworks for LLM and ML systems, including offline and online tests, benchmarks, canaries, and A/B experiments. Work with Product, Data Science, AI Inference Engineering, and Enterprise AI teams to onboard AI use cases into the observability platform from day one. Collaborate with Cloud Engineers across AWS, Azure, and GCP, along with SRE and platform teams, to align AI observability with broader platform observability, capacity planning, and operational management. Support the scaling, monitoring, and operational readiness of AI infrastructure and workloads during major releases and global events. Communicate technical trade-offs, architecture decisions, risks, and recommendations clearly to technical and non-technical stakeholders, including senior leadership. Mentor engineers and share patterns, practices, and lessons learned to raise overall AI solution design and delivery maturity. Required Qualifications 6+ years of progressive experience in solution architecture, technical strategy, senior engineering, platform engineering, or related technical roles. Experience building software