Sr. AI Enablement Engineer

Harvey Harvey · AI Frontier · Remote · IT

This role focuses on integrating, building, and operating AI tooling for internal business processes within an enterprise setting. The engineer will act as a technical partner to various departments, extending existing AI agent workflows, owning technical governance, managing integrations with enterprise systems (MCP, connectors), evaluating new AI capabilities and vendors, and building integration prototypes. The emphasis is on making internal teams more productive with AI and ensuring AI tools are governed, secure, and operationalized as production systems.

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
  • 2+ years building integrations between SaaS systems (HRIS, ERP, contract management, internal platforms, communication tools)
  • 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
  • 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
  • reading DPAs

What the JD emphasized

  • production AI agents
  • technical governance of internal AI tools
  • MCP and connector roadmap
  • AI vendor security and privacy reviews
  • shipping tools that internal teams can extend on their own
  • making other Harvey employees more productive with AI as the actual job
  • production integration of model APIs
  • shipped something real that depends on one
  • built something against it
  • data governance & security best practices
  • evaluating third-party vendors

Other signals

  • integrating AI tooling
  • building AI workflows
  • production AI agents
  • technical governance of AI tools
  • evaluating AI vendors