Senior AI Enablement Engineer, Harvey

• The ideal candidate is a senior IC engineer who is equally comfortable in a code review, a privacy lawyer's office hours, and a People Operations workflow whiteboard — and who sees making other Harvey employees more productive with AI as the actual job
• 5+ years of software or integration engineering experience, with 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, and the kind of 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 don't need to have trained a model; 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 — 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
• Strong DevOps and operational fundamentals — CI/CD, infrastructure-as-code, secrets management, and observability. You treat the integrations and AI tools you ship as production systems: version-controlled, monitored, and safe to change without breaking the teams that depend on them
• Demonstrated experience applying data governance & security best practices
• Demonstrated comfort evaluating third-party vendors — reading DPAs, reasoning about subprocessor chains, asking the right questions about data flows, and translating findings into clear go/no-go recommendations for the business
• Ability to thrive in a fast-paced, high-growth, and global environment with significant ambiguity

Desirable

• We're seeking a Sr. AI Enablement Engineer to advise on, build, integrate, and operate AI tooling for several departments within Harvey
• You'll be the dedicated technical partner who turns AI capability into real workflows that teams use every day, owning the connector and MCP integrations that make those workflows possible, and work with cross-functional teams to evaluate new technologies in this space that we are seeking to adopt
• This is a hands-on technical IC role inside BizTech (Business Technology), partnering closely with teams across G&A, GTM, and Engineering
• You'll spend your time evaluating vendors, prototyping AI workflows for business processes, writing integration code, and shipping tools that internal teams can extend on their own
• Extend and govern AI workflows across the company. Harvey already runs AI agents in production, including automated IT support, with more functions coming online. You'll extend that into People, Legal, Finance, and Workplace: partner with the team that owns each high-friction workflow, ship the AI-powered version, and make sure it's governed and measured
• Own technical governance of internal AI tools. 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, and stand up spend and usage monitoring so cost and access stay visible as adoption scales
• Own the MCP and connector roadmap for enterprise systems. Several MCP integrations are already piloted against core systems. You'll harden those into production and scale the pattern across HRIS, ERP, contract management, ticketing, and knowledge bases — defining the roadmap, building the integrations, and communicating what's available
• Translate emerging AI capability into Harvey's internal roadmap. Track new MCP servers, agent frameworks, and new agentic features across the platforms in use, and make a clear, opinionated call on what to adopt, ignore, or wait on for relevant use cases
• Run AI vendor security and privacy reviews as a structured workstream. AI-adjacent vendor evaluations land on BizTech regularly and are handled ad hoc today. You'll build a documented intake, a reusable AI vendor risk framework, and a clear sign-off path, partnering with Privacy, Security, and Legal
• Build integration prototypes and reference architectures. Ship working examples that internal teams can extend on their own, so you're not the bottleneck for every new workflow
• Be the technical partner of choice for G&A teams. 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