Senior Forward Deployed AI Engineer, Smartsheet

• 6–10+ years production software engineering, including 3+ years deploying AI/ML to production. Deep Python; strong TypeScript/JavaScript
• Proven track record leading complex enterprise technical engagements. You’ve managed customer relationships, navigated security reviews, and delivered production systems organizations depend on
• Deep production LLM experience: multi-agent orchestration, MCP/tool-use, RAG optimization, evaluation frameworks, agent deployment at scale
• System architecture expertise spanning enterprise systems, cloud infrastructure, and AI services explainable to a CIO and a junior engineer
• Demonstrated ability to create reusable technical assets that others use in production. You think in platforms, not projects
• Strong written and verbal communication. Architecture documents, runbooks, strategic memos, training materials. Your Deployment Kits are your portfolio
• You leave teams better than you found them. You make sure the people you work with understand what was built, why it works, and how to own it without you
• Ability to travel. This role requires domestic and international travel approximately 25–50% of the time
• Legally eligible to work in the U.S. on an ongoing basis
• BS or MS in Computer Science, a related field, or equivalent industry experience

Desirable

• You independently lead the most complex AI deployments Smartsheet undertakes
• You own the full engagement lifecycle from technical discovery through production deployment through solutions org handoff
• You architect multi-agent solutions, design client-personalized MCP resource packs, and build the Deployment Kits that transform how 200+ solutions consultants and partners operate
• You mentor junior FDEs, drive the intelligence loop, and present field findings at weekly Applied AI strategy sessions
• You are building a function, not filling a role
• Lead complex, multi-system AI deployments end-to-end scope, architect, build, validate, and manage the customer relationship throughout
• Own the AI workshop program for your pod, customize modules per customer, lead technical sessions, translate outputs into production requirements, evolve content from field learning
• Architect multi-agent solutions selecting the right coordination pattern for each customer’s workflow characteristics and compliance requirements
• Design client-specific and industry-specific MCP resource packs that serve personalized intelligence from the server so every connected AI surface gets smarter for that customer automatically
• Own Deployment Kit quality for your pod. If a kit is not documented well enough for a solutions consultant with no engineering background to follow, it isn’t done
• Lead Solutions Enablement Sprints: transfer AI deployment patterns to solutions consultants and partners with training materials and certification criteria
• Drive the intelligence loop: author strategic memos, present field findings weekly, contribute Agent Bricks and Skills, file RFCs for platform improvements
• Mentor junior FDEs on customer engagements, code reviews, and Deployment Kit quality