Lead AI Forward Engineer

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, and extensible. You will evaluate emerging AI technologies, define repeatable patterns, and help build new capabilities through hands-on implementation, mentorship, and shared standards. About the Role Solution Architecture and Delivery 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. AI System Design and Technology Strategy 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. Cross Functional Partnership and Influence 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. 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). Proficiency in  Python  (strongly preferred) and ability to prototype and validate designs with hands-o ... (truncated, view full listing at source)