Senior Product Manager

Relativity is a leading legal data intelligence company specializing in AI-powered cloud platforms for legal data organization and insights, serving law firms, corporations, and government agencies. The Agentic Platform team develops and operates the shared agent harness that supports scalable, reliable, and governed agentic solutions across Relativity's AI products.

Required:

• 5+ years experience in product management, preferably owning platform or infrastructure products.
• Experience building AI-powered or agentic systems in production, including orchestration, tool usage, multi-step workflows.
• Experience with build-versus-buy decisions and influencing architectural direction with engineering.
• Strong analytical skills using telemetry, analytics, experimentation, and data.
• Experience influencing priorities across teams without direct authority.
• Excellent communication skills.
• Bachelor's degree in Computer Science, Engineering, or equivalent.

Preferred:

• Familiarity with tool access and interoperability protocols like MCP and A2A.
• Knowledge of agent orchestration frameworks such as LangGraph.
• Familiarity with MLOps infrastructure including registries, tracing, observability.
• Understanding of modern AI platform architectures and agent lifecycle management.

• Develop understanding of workflows, dependencies, and failure points in designing, building, testing, and deploying agents and skills.
• Engage with product teams to identify gaps, validate requirements, and drive platform improvements.
• Translate stakeholder feedback into product vision, strategy, and roadmap.
• Monitor external agent ecosystem (agent SDKs, MCP, A2A, orchestration patterns) to inform decisions.
• Own product strategy and execution for the core harness and reference agent.
• Define and track success metrics for platform adoption, quality, and time-to-build.
• Lead complex cross-functional initiatives spanning engineering, applied science, and product.
• Prioritize investments balancing scalability and product needs.
• Drive integration with AI Platform capabilities like model/agent registries, tracing, observability, evaluation.
• Define exposure and discovery of harness-hosted agents.
• Partner with Applied Science teams on platform AI capabilities.
• Improve onboarding and reduce deployment time for agents.
• Establish governance and lifecycle management practices for shared infrastructure.
• Shape long-term platform strategy including skills registries, low-code authoring, multi-model support, agent handoff.
• Use adoption metrics and operational insights to improve the platform continuously.