Applied AI Engineer, Handshake AI Enterprise

Handshake Handshake · Enterprise · San Francisco, CA · Engineering

Applied AI Engineer role focused on building and deploying production-grade AI agents within enterprise customer environments. The role involves understanding customer business needs, developing agents, running evaluations, and iterating on performance to drive measurable business impact. Requires backend engineering depth and experience with AI/ML systems in production.

What you'd actually do

  1. Contribute to customer deployments as part of a small team, developing genuine understanding of their business and workflows
  2. Build and deploy production-grade agents tailored to specific customer use cases
  3. Design and run evals to measure agent performance; iterate and hill-climb until results move
  4. Work closely with Senior Engineers, Product, and Platform teammates to ship cohesive, reliable solutions
  5. Contribute to engineering best practices, agent patterns, and reusable frameworks as the team scales

Skills

Required

  • 2-5 years of engineering experience
  • backend depth
  • exposure to AI or ML systems in production
  • Hands-on experience building and shipping software in production
  • Familiarity with agent architectures or LLM-based systems
  • Comfort with ambiguity
  • Strong communication skills
  • High ownership mentality

Nice to have

  • Experience building or shipping AI applications in production
  • Exposure to enterprise environments or customer-facing engineering roles
  • Familiarity with evals, prompt engineering, or AI quality measurement
  • Domain knowledge in a specific enterprise vertical (recruiting, finance, ops, legal, etc.)

What the JD emphasized

  • production-grade AI agents
  • production-grade agents
  • running evals
  • ship cohesive, reliable solutions
  • AI or ML systems in production
  • shipping software in production
  • whether it actually works
  • customer deployments
  • enterprise customer environments

Other signals

  • building and deploying production-grade AI agents
  • running evals
  • iterating until performance actually moves
  • customer deployments
  • enterprise customer environments