Senior Forward Deployed AI Engineer (remote Eligible)

Smartsheet Smartsheet · Seattle · United States · Engineering - Developers

Senior AI Engineer responsible for leading complex, multi-system AI deployments end-to-end, architecting multi-agent solutions, designing personalized intelligence packs, and owning Deployment Kit quality. The role involves mentoring junior engineers, driving the intelligence loop, and enabling solutions consultants and partners.

What you'd actually do

  1. Lead complex, multi-system AI deployments end-to-end scope, architect, build, validate, and manage the customer relationship throughout.
  2. 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.
  3. Architect multi-agent solutions selecting the right coordination pattern for each customer’s workflow characteristics and compliance requirements.
  4. 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.
  5. 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.

Skills

Required

  • Python
  • TypeScript
  • JavaScript
  • LLM experience
  • multi-agent orchestration
  • tool-use
  • RAG optimization
  • evaluation frameworks
  • agent deployment at scale
  • System architecture
  • enterprise systems
  • cloud infrastructure
  • AI services
  • communication skills
  • technical writing

Nice to have

  • founding or scaling an FDE/solutions engineering function
  • Technical founder background
  • Databricks
  • AWS Bedrock
  • Temporal
  • healthcare domain knowledge
  • financial services domain knowledge
  • government domain knowledge
  • Agent framework contributions (Strands SDK, LangChain, open-source MCP servers)

What the JD emphasized

  • production software engineering
  • deploying AI/ML to production
  • Deep Python
  • strong TypeScript/JavaScript
  • Proven track record leading complex enterprise technical engagements
  • Deep production LLM experience
  • multi-agent orchestration
  • MCP/tool-use
  • RAG optimization
  • evaluation frameworks
  • agent deployment at scale
  • System architecture expertise
  • enterprise systems
  • cloud infrastructure
  • AI services
  • Demonstrated ability to create reusable technical assets
  • platforms, not projects
  • Strong written and verbal communication
  • Architecture documents
  • runbooks
  • strategic memos
  • training materials
  • Deployment Kits

Other signals

  • Deploying AI/ML to production
  • multi-agent orchestration
  • agent deployment at scale