Note: The job is a remote job and is open to candidates in USA. Thomson Reuters provides trusted content and technology that help professionals and organizations make informed decisions across legal, tax, accounting, compliance, government, and media. The Lead AI Forward Engineer designs and delivers AI-powered solutions for CIO technology teams, owning solution architecture from concept through production while establishing scalable patterns, observability, governance, and operational standards. The role also evaluates emerging AI technologies, partners with cross-functional teams, and mentors engineers.
Responsibilities
- Identify high-impact opportunities to apply AI automation and intelligent agents across CIO technology teams
- Design end-to-end AI solutions, including workflows, integration patterns, data flows, and operational considerations
- Guide implementation from prototype to production, ensuring solutions meet reliability, security, and compliance expectations
- Define reusable architectural patterns and reference designs to enable broader adoption across teams
- Build scalable pipelines to collect and analyze inference‑ and workflow‑level telemetry, integrating with TR's data backbone
- Develop dashboards and reports providing clear visibility into performance, reliability, safety, and cost
- Ensure compliance with TR's AI standards for monitoring, governance, privacy, and auditability
- Evaluate and recommend AI/ML technologies and platforms (LLM orchestration, agentic frameworks, cloud AI services) based on capability, cost, risk, and fit
- Design flexible architectures that can evolve with model/provider changes and emerging AI capabilities
- Apply sound judgment on when AI is appropriate vs. when simpler automation or traditional engineering approaches are better
- Establish and track SLOs/SLIs for critical AI services to meet enterprise reliability and compliance requirements
- Integrate AI observability tooling into CI/CD so new models, prompts, and workflows are automatically enrolled in monitoring and evaluation
- Develop automated guardrails and policy enforcement (e.g., limits, anomaly detection, abuse/failure pattern detection) with cloud engineering and security teams
- Partner with engineering teams, service owners, and stakeholders to translate business needs into technical requirements and solution designs
- Communicate trade-offs and design decisions clearly to both technical and non-technical audiences, including senior leadership when needed
- Mentor engineers and share patterns, practices, and lessons learned to raise overall AI solution design maturity
- Partner with Product, Data Science and AI teams to design and run evaluation frameworks for LLMs/ML models (offline/online tests, benchmarks, canaries, A/B experiments)
- Work with Product, Data Science, AI Inference Engineering, and Enterprise AI teams to onboard new AI use cases into the observability platform from day one
- Collaborate with Cloud Engineers (AWS, Azure and GCP) and SREs to align AI observability with broader platform observability and capacity management
- Support scaling and monitoring of AI infrastructure and workloads during major releases and global events
Skills
- 6+ years of experience with progression in solution architecture, technical strategy, or senior engineering roles
- Experience building software prototypes and taking solutions to production in ambiguous, low-precedent environments
- Strong solution design/architecture capability: end-to-end system design, integration patterns, API thinking, and operational design
- Working knowledge of AI/ML and LLM application patterns, including: LLM capabilities/limitations, prompt design, orchestration approaches, and agent workflows; Practical trade-offs (latency, quality, cost, safety, reliability); Production AI systems and observability challenges (prompting, context windows, RAG, hallucinations, provider variability)
- Strong communication skills: ability to document designs, influence decisions, and align diverse stakeholders
- Proficiency in Python (strongly preferred) and ability to prototype and validate designs with hands-on technical work
- Cloud architecture familiarity in AWS, Azure, or GCP, including common service patterns and enterprise constraints
- Familiarity with LLM frameworks and patterns (e.g., LangChain, LlamaIndex), RAG/vector search concepts, and enterprise integration considerations
- Experience with DevOps/Platform Engineering/SRE principles and designing for operational excellence
- Exposure to enterprise service management (e.g., ServiceNow/ITSM), security architecture, and compliance-oriented environments
Benefits
- Flexible hybrid working environment for office-based roles
- Work from anywhere for up to 8 weeks per year
- Continuous learning and skill development through Grow My Way programming and a skills-first approach
- Flexible vacation
- Two company-wide Mental Health Days off
- Access to the Headspace app
- Retirement savings
- Tuition reimbursement
- Employee incentive programs
- Resources for mental, physical, and financial wellbeing
- Two paid volunteer days off annually
- Opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives
- Market competitive health, dental, vision, disability, and life insurance programs
- Competitive 401k plan with company match
- Competitive vacation, sick and safe paid time off
- Paid holidays, including two company mental health days off
- Parental leave
- Sabbatical leave
- Optional hospital, accident and sickness insurance paid 100% by the employee
- Optional life and AD&D insurance paid 100% by the employee
- Flexible Spending and Health Savings Accounts
- Fitness reimbursement
- Access to Employee Assistance Program
- Group Legal Identity Theft Protection benefit paid 100% by employee
- Access to 529 Plan
- Commuter benefits
- Adoption & Surrogacy Assistance
- Access to Employee Stock Purchase Plan
- Annual Bonus based on a combination of enterprise and individual performance
Company Overview
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