Senior Software Engineer, Agentic Infrastructure

Handshake · Enterprise · San Francisco, CA · Engineering

Senior Software Engineer to architect and build the foundational systems for AI agents, including tool use, memory, and multi-step reasoning. The role involves designing evaluation and observability frameworks, establishing engineering standards for agentic development, and partnering with ML/product teams to ship agent-powered features.

What you'd actually do

  1. Architect & build the core agent orchestration layer — including tool use, memory, & multi-step reasoning systems — that powers AI-driven features for 20M+ users & 1M+ employers across Handshake's platform
  2. Design evaluation, observability, & reliability frameworks that ensure agent behavior is safe, auditable, & production-ready at scale
  3. Establish engineering standards for agentic development across Handshake's platform teams, enabling every engineer to build with AI
  4. Partner with ML, product, & platform engineers to ship agent-powered features from infrastructure to production at Olympic pace

Skills

Required

  • 4-7 years of backend engineering experience
  • strong proficiency in NodeJS, Typescript, &/or Python
  • Experience designing & operating distributed systems at significant scale for developer experience
  • Hands-on experience building with LLM orchestration frameworks (LangChain, LlamaIndex, or similar)
  • Deep understanding of agent architectures: tool use, multi-step reasoning, memory management, & evaluation
  • Experience with cloud infrastructure (AWS, GCP, or Azure)
  • infrastructure-as-code tools like Terraform
  • Experience with Node, JavaScript, CICD, Docker
  • A track record of shipping production systems with high reliability, low latency, & strong observability
  • Strong communication skills & ability to drive alignment across engineering & cross-functional partners

Nice to have

  • Open-source contributions to LLM or agent frameworks
  • Prior experience at an AI-native company or on a dedicated AI platform team

What the JD emphasized

  • Hands-on experience building with LLM orchestration frameworks (LangChain, LlamaIndex, or similar)
  • Deep understanding of agent architectures: tool use, multi-step reasoning, memory management, & evaluation
  • A track record of shipping production systems with high reliability, low latency, & strong observability

Other signals

  • building foundational systems for AI agents
  • architecting core agent orchestration layer
  • shipping agent-powered features