Senior Applied AI Engineer, Handshake AI Enterprise

Handshake Handshake · Enterprise · San Francisco, CA · Engineering

Senior Applied AI Engineer role focused on embedding within enterprise customer environments to build and deploy production-grade AI agents. The role involves defining AI-driven solutions, owning end-to-end delivery, designing and running evaluations, and iterating on performance. It requires strong applied AI and backend experience, with a focus on real-world application and systems thinking.

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
  • strong applied AI and 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
  • 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

Other signals

  • building and deploying agents
  • designing evals
  • iterating until performance actually moves
  • production-grade AI agents
  • real-world experience building and shipping AI applications in production