WHAT YOU’LL DO

• Develop a deep understanding of the workflows, abstractions, dependencies, and failure points involved in designing, building, testing, and deploying agents and skills.

• Engage directly with product teams, including aiR Assist and other AI initiatives, to identify gaps, validate requirements, and drive platform improvements.

• Translate stakeholder feedback into a clear product vision, strategy, and roadmap for the agent harness and reference agent.

• Monitor the external agent ecosystem, including agent SDKs, MCP, A2A, and orchestration patterns, to inform product and architectural decisions.

• Own product strategy and execution for both the core harness and the reference agent implementation.

• Define and track success metrics related to platform adoption, reference agent quality, and time-to-build for consuming teams.

• Lead complex cross-functional initiatives spanning engineering, applied science, and product organizations.

• Prioritize investments across platform capabilities and reference agent enhancements while balancing platform scalability and product needs.

• Drive integration of the harness with adjacent AI Platform capabilities such as model and agent registries, tracing, observability, and evaluation systems.

• Define how harness-hosted agents are exposed and discovered across the broader AI platform experience.

• Partner with Applied Science teams to support evaluation, intent detection, and other AI capabilities required by the platform.

• Improve onboarding experiences and reduce the time required for teams to deploy agents on the harness.

• Establish governance standards and lifecycle management practices for shared agent infrastructure.

• Shape the long-term platform strategy, including skills registries, low-code authoring experiences, multi-model support, and agent-to-agent handoff capabilities.

• Use adoption metrics, operational insights, and incident learnings to continuously evolve and improve the platform.

WHAT WE’RE LOOKING FOR

Required

• 5+ years of experience in product management, ideally owning platform or infrastructure products used by internal teams.

• Experience building AI-powered or agentic systems in production environments, including orchestration, tool usage, or multi-step reasoning workflows.

• Experience making and defending build-versus-buy decisions and influencing architectural direction alongside engineering teams.

• Strong analytical mindset with hands-on experience using telemetry, analytics, experimentation, and data to inform product decisions.

• Experience influencing roadmaps and priorities across teams without direct authority.

• Excellent written and verbal communication skills.

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.

Preferred

• Familiarity with tool access and interoperability protocols such as MCP (Model Context Protocol) and A2A.

• Working knowledge of agent orchestration frameworks such as LangGraph and an understanding of their tradeoffs.

• Familiarity with MLOps infrastructure, including model or artifact registries, tracing, and observability tooling.

• Understanding of modern AI platform architectures and agent lifecycle management.

WHY WE COULD BE A GREAT FIT

Impactful Mission

• Build systems that help customers organize data, discover the truth, and act on it in high-stakes legal matters.

Engineering at Scale

• Work on distributed, cloud-native systems that process large volumes of data.

Cutting-Edge Technology

• Build with AI, cloud platforms, and scalable architectures shaping legal tech.

Growth and Ownership

• Gain experience owning systems end-to-end across cloud and distributed environments.

Collaborative Culture

• Work in a team focused on knowledge sharing and continuous improvement.

Inclusive Environment

• Diverse perspectives create stronger teams and better outcomes.

Compensation and Benefits

• Competitive salary, benefits, DTO, parental leave, and equity program.