Lead AI Forward Engineer

About the role

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.

Responsibilities

• Evaluate emerging AI technologies, define repeatable patterns, and help build new capabilities through hands-on implementation, mentorship, and shared standards.
• 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.
• Develop 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.
• 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.
• Mentor engineers and share patterns, practices, and lessons learned to raise overall AI solution design maturity.
• Work 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).
• 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.

Requirements

Strong solution design/architecture capability: end-to-end system design, integration patterns, API thinking, and operational design.

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-on technical work.

Cloud architecture familiarity in AWS, Azure, or GCP, including common service patterns and enterprise constraints.

Knowledge of distributed systems, microservices, CI/CD, and cloud-native architectures.

Strong communication skills: ability to document designs, influence decisions, and align diverse stakeholders.

Qualifications

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.

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.

Demonstrated technical leadership through mentoring, architectural governance, or cross-team enablement.

Skills

Working knowledge of AI/ML and LLM application patterns, including: LLM capabilities/limitations, prompt design, orchestration approaches, and agent workflows.

Production AI systems and observability challenges (prompting, context windows, RAG, hallucinations, provider variability).

Cloud architecture familiarity in AWS, Azure, or GCP, including common service patterns and enterprise constraints.

Knowledge of distributed systems, microservices, CI/CD, and cloud-native architectures.

Strong communication skills: ability to document designs, influence decisions, and align diverse stakeholders.

Benefits

Flexible Work Model: Hybrid model with 2-3 days a week in the office.

Flexibility & Work-Life Balance: Flex My Way policies including Mental Health Days off, access to Headspace app, and paid volunteer days.

Career Development and Growth: Comprehensive training and development programs, including the Grow My Way initiative.

Industry Competitive Benefits: Market competitive health, dental, vision, disability, and life insurance programs, a competitive 401k plan with company match, and a variety of additional benefits such as fitness reimbursement, Employee Assistance Program, and tuition reimbursement.

Culture: Global recognition for inclusion and belonging, flexibility, work-life balance, and more.

Social Impact: Opportunities to engage in pro-bono consulting projects and ESG initiatives.

Make a Real-World Impact: Support for justice, truth, and transparency through AI-enabled solutions.

Equal Employment Opportunity Employer: Committed to diversity and inclusion, providing accommodations for applicants with disabilities and veterans.