Sr. AI Enablement Engineer
$134k - $200k • Remote • San Francisco • FullTime
Posted 14d ago
About the job
Harvey is seeking a Sr. AI Enablement Engineer to join their Business Technology team. This role is a hands-on technical position focused on advising on, building, integrating, and operating AI tooling for various departments within the company. The engineer will be responsible for turning AI capabilities into practical workflows that teams use daily, owning critical integrations, and evaluating new AI technologies for adoption. The ideal candidate is a senior individual contributor comfortable collaborating with technical and non-technical stakeholders, and who is driven by making other employees more productive through AI.
Responsibilities
- Extend and govern AI workflows across departments like People, Legal, Finance, and Workplace.
- Own the technical governance of internal AI tools, including defining publishing processes, scoping rules, and review cadences.
- Manage the MCP and connector roadmap for enterprise systems, hardening piloted integrations and scaling them across HRIS, ERP, contract management, and knowledge bases.
- Translate emerging AI capabilities into Harvey's internal roadmap by tracking new MCP servers, agent frameworks, and agentic features.
- Conduct structured AI vendor security and privacy reviews, building documented intake processes and risk frameworks.
- Build integration prototypes and reference architectures for internal teams to extend.
- Serve as the technical partner for G&A teams, facilitating AI workflow development in areas like NetSuite, Workday, and contract intake.
Requirements
- 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, identity, and webhook architectures.
- Practical experience with LLM-based applications and AI tooling, including 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 skills, particularly with non-technical partners.
- Strong DevOps and operational fundamentals, including CI/CD, infrastructure-as-code, secrets management, and observability.
- Demonstrated experience applying data governance and security best practices.
- Demonstrated comfort evaluating third-party vendors, including reading DPAs and reasoning about subprocessor chains.