Lead AI Forward Engineer

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 prototypes and delivering solutions to production in ambiguous, low-precedent environments.
Strong end-to-end solution design and architecture capability, including integration patterns, APIs, data flows, distributed systems, and operational design.
Working knowledge of AI/ML and LLM application patterns, including LLM capabilities and limitations, prompt design, orchestration approaches, agent workflows, RAG, vector search, and enterprise integration considerations.
Practical understanding of production AI system trade-offs, including latency, quality, cost, safety, reliability, context-window constraints, hallucinations, and provider variability.
Experience designing, building, operating, or observing production AI systems and associated telemetry, monitoring, evaluation, and operational workflows.
Proficiency in Python, with the ability to prototype, validate, and support solution designs through hands-on technical work.
Cloud architecture familiarity in AWS, Azure, or GCP, including common service patterns, enterprise constraints, and security considerations.
Knowledge of microservices, distributed systems, CI/CD, cloud-native architectures, and API-driven integration approaches.
Experience with DevOps, Platform Engineering, or SRE principles and designing systems for operational excellence.
Strong communication skills, with the ability to document designs, influence decisions, and align diverse technical and business stakeholders.
Demonstrated technical leadership through mentoring, architectural governance, cross-team enablement, or shared standards.
Preferred Qualifications
Familiarity with LLM frameworks and patterns, such as LangChain, LlamaIndex, or comparable technologies.
Experience with AI observability, including telemetry pipelines, dashboards, alerting, service-level indicators, service-level objectives, and evaluation frameworks.
Experience designing AI guardrails, policy enforcement, anomaly detection, or AI safety and reliability controls.
Exposure to enterprise service management platforms, such as ServiceNow or comparable ITSM tools.
Exposure to security architecture, privacy, compliance-oriented environments, enterprise governance, and auditability requirements.
Experience collaborating with Product, Data Science, AI Inference Engineering, Enterprise AI, Cloud Engineering, SRE, or Platform Engineering teams.
Experience supporting the scaling and monitoring of AI infrastructure and workloads in large, complex enterprise environments.
#LI-LP2
What’s in it For You?
Hybrid Work Model: We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected.
Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver r