Senior Forward Deployed Engineer, Handshake AI Enterprise

Handshake Handshake · Enterprise · San Francisco, CA · Engineering

Senior Forward Deployed Engineer to embed within enterprise customer environments, define AI-driven solutions, build and deploy production-grade AI agents, and design/run evals to measure performance. The role requires full-stack ownership, deep understanding of customer business, and iteration until performance improves. Emphasis on real-world AI application shipping and systematic improvement of AI performance.

What you'd actually do

  1. Embed directly with enterprise customers as part of a small deployment team, developing deep understanding of their business and workflows
  2. Define AI-driven solutions based on real business needs — not theoretical use cases
  3. Build and deploy production-grade agents tailored to specific customer use cases, owning end-to-end delivery
  4. Design and run evals to measure agent performance; iterate and hill-climb until results move
  5. Become a domain expert in your customer's vertical and use that expertise to build better AI over time

Skills

Required

  • 5–6+ years of engineering experience, full-stack with strong backend depth
  • Real-world experience building and shipping AI applications in production
  • Strong understanding of agent architectures, evals, and how to measure and systematically improve AI performance
  • Comfort working directly with enterprise customers in ambiguous, high-stakes environments
  • Strong systems thinking
  • High ownership mentality
  • Strong communication skills

Nice to have

  • Background at a company with a forward deployment or field engineering model
  • Experience building multi-agent systems in production
  • Domain expertise in a specific enterprise vertical (recruiting, finance, ops, legal, etc.)
  • Familiarity with enterprise security requirements, VPC deployments, or on-prem configurations
  • History of building evals infrastructure or AI quality measurement tooling

What the JD emphasized

  • production-grade AI agents
  • designing evals
  • measure agent performance
  • systematically improve AI performance
  • real-world experience building and shipping AI applications in production

Other signals

  • building and deploying production-grade AI agents
  • designing and running evals to measure agent performance
  • embedding directly with enterprise customers