AI Delivery Lead
About the role
About Harbor Labs
Harbor Labs is a new team within Harbor focused on solving hard AI implementation problems in the legal market. We have strong hypotheses about what works, but we're building this like a startup: learning as we go, adjusting direction based on what the market tells us, and staying flexible about how we operate.
We're looking for an AI Delivery Lead to join at the ground floor. This role sits at the critical intersection between our engineering team and our clients, translating ambiguous business problems into concrete AI solutions, then driving them across the finish line.
This is not a management role. You will be hands-on: building AI-powered applications and solutions, designing transformational workflows, running workshops, configuring systems, testing with real data, and working shoulder-to-shoulder with clients to make AI actually useful in their day-to-day work.
What Makes This Different
Our edge is combining deep legal domain experience with product sensibility and strong engineering. We understand how legal teams actually work, what change management looks like in risk-averse environments, and how to build software that people will use. We also know the common pitfalls of enterprise software in traditionally low-innovation areas like law, and we're determined to avoid them.
We focus on hard, novel problems where standard platforms fall short. The work is ambiguous, the solutions aren't obvious, and that's the point.
What You'll Do
- Own client delivery end-to-end. Lead engagements from problem definition through production deployment. You're accountable for outcomes, not just activity.
- Translate between business and engineering. Work with legal teams to understand what they actually need (not just what they say they want), then spec solutions our engineers can build.
- Design AI-native workflows. Figure out where AI fits in existing processes, what needs to change, and how to make the human + AI collaboration actually work.
- Get your hands dirty with the product. Configure systems, curate and structure knowledge sources, write prompts, design test cases, and validate outputs against real-world data.
- Drive integrations and architecture decisions. Work with engineers to figure out how AI fits into client tech stacks: APIs, data flows, security requirements.