Sr. AI Enablement Engineer

Harvey Harvey · AI Frontier · New York, NY · IT

This role focuses on enabling internal teams by building, integrating, and operating AI tooling, specifically agentic workflows and MCP integrations with enterprise systems. The engineer will be responsible for technical governance, vendor reviews, and translating emerging AI capabilities into practical internal applications, acting as a technical partner for various business departments.

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 (HRIS, ERP, contract management, internal platforms, communication tools)
  • Hands-on experience with API integration patterns, OAuth and identity, webhook architectures
  • Practical experience with LLM-based applications and AI tooling — prompt design, agent workflows, retrieval, evaluation, or production integration of model APIs
  • Working knowledge of the Model Context Protocol (MCP) or comparable agent-tool integration patterns
  • Strong communication and stakeholder-management instincts
  • Strong DevOps and operational fundamentals — CI/CD, infrastructure-as-code, secrets management, and observability
  • Demonstrated experience applying data governance & security best practices
  • Demonstrated comfort evaluating third-party vendors

Nice to have

  • you don't need to have trained a model
  • If you haven't shipped MCP yet, you've at minimum read the spec and built something against it
  • you can be the most technical person in a Privacy review and the most pragmatic person in an engineering one in the same afternoon, sitting between Finance, People, Legal, IT, and Privacy without losing context

What the JD emphasized

  • partner with the team that owns each high-friction workflow
  • ship the AI-powered version
  • make sure it's governed and measured
  • Define the publishing process, scoping rules, and review cadence for the plugin and skill marketplace
  • Own the pre-deployment security-review path for new AI tools
  • stand up spend and usage monitoring
  • defining the roadmap, building the integrations, and communicating what's available
  • make a clear, opinionated call on what to adopt, ignore, or wait on
  • build a documented intake, a reusable AI vendor risk framework, and a clear sign-off path
  • Ship working examples that internal teams can extend on their own
  • Sit next to Finance on a NetSuite workflow, next to People on a Workday-flavored automation, next to Legal on a contract intake flow, and next to Privacy on a vendor review
  • Translate fluently in every direction
  • building integrations between SaaS systems
  • glue work that makes enterprise systems actually talk to each other reliably
  • Practical experience with LLM-based applications and AI tooling — prompt design, agent workflows, retrieval, evaluation, or production integration of model APIs
  • you do need to have shipped something real that depends on one
  • Working knowledge of the Model Context Protocol (MCP) or comparable agent-tool integration patterns
  • If you haven't shipped MCP yet, you've at minimum read the spec and built something against it
  • Strong communication and stakeholder-management instincts, especially with non-technical partners
  • Strong DevOps and operational fundamentals
  • You treat the integrations and AI tools you ship as production systems
  • Demonstrated experience applying data governance & security best practices
  • Demonstrated comfort evaluating third-party vendors

Other signals

  • Extending AI workflows
  • Owning technical governance of internal AI tools
  • Owning the MCP and connector roadmap for enterprise systems
  • Translating emerging AI capability into internal roadmap
  • Running AI vendor security and privacy reviews
  • Building integration prototypes and reference architectures