Sr. AI Enablement Engineer

harvey; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

PVH (Tommy Hilfiger/Calvin Klein) seeks a Sr. AI Enablement Engineer to advise on, build, integrate, and operate AI tooling across Harvey's departments.

You will own the connector and MCP integrations that enable these workflows and partner with cross‑functional teams to evaluate new technologies. You will govern AI tool publishing, define security review paths, and scale enterprise integrations across HRIS, ERP, and other systems, while aligning AI capabilities with the company roadmap and

Qualifications

• 5+ years of software or integration engineering experience, with hands-on SaaS integrations across HRIS, ERP, contract management, and internal platforms.
• Experiences with API integration patterns, OAuth, webhook architectures, and enterprise system glue work.
• Practical experience with LLM-based applications, prompt design, agent workflows, retrieval, and production integration of model APIs.

Responsibilities

• Govern and extend AI workflows across Harvey's departments; ship AI-powered versions for multiple teams.
• Own technical governance of internal AI tools, publishing rules, and review cadence for plugin and skill marketplace.
• Define pre-deployment security-review path for new AI tools and establish spend/usage monitoring.
• Own MCP and connector roadmap for enterprise systems; scale production integrations across HRIS, ERP, contract management, ticketing, and knowledge bases.
• Translate emerging AI capabilities into Harvey’s internal roadmap; evaluate MCP servers, agent frameworks, and features for adoption.
• Run AI vendor security and privacy reviews; build a reusable risk framework with Privacy, Security, and Legal.
• Build integration prototypes and reference architectures; ship working examples for internal teams.
• Serve as the technical partner of choice for G&A teams, collaborating with Finance, People, Legal, and Privacy.

Skills

API integration

OAuth

Webhook architectures

LLM applications

Agent workflows

Enterprise system glue

Model Context

Job description

Role Overview

We're seeking a Sr. AI Enablement Engineer to advise on, build, integrate, and operate AI tooling across Harvey's departments. You will serve as the dedicated technical partner who turns AI capability into real workflows, owning the connector and MCP integrations that enable these workflows, and collaborate with cross‑functional teams to evaluate new technologies.

What You'll Do

• Extend and govern AI workflows across the company, shipping AI‑powered versions for People, Legal, Finance, and Workplace teams.
• Own technical governance of internal AI tools, defining publishing processes, scoping rules, and review cadence for the plugin and skill marketplace.
• Define the pre‑deployment security‑review path for new AI tools and establish spend and usage monitoring.
• Own the MCP and connector roadmap for enterprise systems, scaling production integrations across HRIS, ERP, contract management, ticketing, and knowledge bases.
• Translate emerging AI capability into Harvey’s internal roadmap, evaluating new MCP servers, agent frameworks, and features for adoption.
• Run AI vendor security and privacy reviews, building a documented intake and reusable risk framework in partnership with Privacy, Security, and Legal.
• Build integration prototypes and reference architectures, shipping working examples for internal teams.
• Serve as the technical partner of choice for G&A teams, collaborating closely across Finance, People, Legal, and Privacy.

What You Have

• 5+ years of software or integration engineering experience, with at least 2 years building integrations between SaaS systems such as HRIS, ERP, contract management, and internal platforms.
• Hands‑on experience with API integration patterns, OAuth, webhook architectures, and enterprise system glue work.
• Practical experience with LLM‑based applications and AI tooling—prompt design, agent workflows, retrieval, and production integration of model APIs.

Working knowledge of the Model Context.