Sr. AI Enablement Engineer

Harvey Harvey · AI Frontier · San Francisco, CA · IT

This role focuses on enabling internal teams by building, integrating, and operating AI tooling. It involves extending existing AI workflows, owning technical governance of internal AI tools, managing the roadmap for enterprise system integrations, translating emerging AI capabilities into the internal roadmap, running vendor security and privacy reviews, building integration prototypes, and acting as a technical partner for various G&A teams. The role requires hands-on software and integration engineering experience, practical experience with LLM-based applications, and strong DevOps fundamentals.

What you'd actually do

  1. Extend and govern AI workflows across the company.
  2. Own technical governance of internal AI tools.
  3. Own the MCP and connector roadmap for enterprise systems.
  4. Translate emerging AI capability into Harvey's internal roadmap.
  5. Run AI vendor security and privacy reviews as a structured workstream.

Skills

Required

  • 5+ years of software or integration engineering experience
  • at least 2 years building integrations between SaaS systems
  • API integration patterns
  • OAuth and identity
  • webhook architectures
  • LLM-based applications and AI tooling
  • prompt design
  • agent workflows
  • retrieval
  • evaluation
  • production integration of model APIs
  • Model Context Protocol (MCP) or comparable agent-tool integration patterns
  • communication and stakeholder-management instincts
  • DevOps and operational fundamentals
  • CI/CD
  • infrastructure-as-code
  • secrets management
  • observability
  • data governance & security best practices
  • evaluating third-party vendors

Nice to have

  • reading the MCP spec and built something against it

What the JD emphasized

  • AI agents in production
  • shipping something real that depends on one
  • Model Context Protocol (MCP)
  • AI-adjacent vendor evaluations

Other signals

  • AI agents in production
  • shipping AI-powered versions of workflows
  • technical governance of internal AI tools
  • evaluating vendors
  • prototyping AI workflows
  • shipping tools that internal teams can extend