Product Manager- AI

Role Overview

Our AI platform is changing how our people deliver work, bringing generative and agentic AI into the everyday flow of practitioners across the firm. It is not a single application but a growing family of products conversational assistants, agentic workflows, knowledge and retrieval, content creation, and the shared skills and services that power them. We are looking for a Product Manager to own one of these products end to end.

You will lead the full product lifecycle for your product, from discovery through launch and iteration, translating how people actually work into capabilities that are useful, reliable, and trusted. You will operate at the intersection of product, design, and engineering, working within the platform's shared architecture and standards rather than building them alone.

This is an AI-native role in practice: we expect you to use AI to do the job, not just manage it as a feature. You will prototype, draft and stress-test requirements, and accelerate your own workflow with AI tools daily. As one of the platform's most demanding users, you will also work inside the very products you build. Fluency in modern AI and the instinct to reach for it matter here as much as classic product judgment. The product you own will be matched to your strengths and to where the platform needs an owner.

Key Responsibilities

Product Strategy & Ownership

Lead the full product lifecycle for your product discovery, definition, launch, and iteration.

Own the roadmap across its components and features, and connect it to the platform's broader goals

Conduct user and stakeholder research to surface the highest-value problems, and prioritize the features that solve them.

Translate ambiguous needs into clear requirements and a defensible delivery plan.

Turn repeatable patterns from real delivery into reusable, scalable product capabilities.

Design & Architecture (Within the Platform)

Partner with Engineering and Design to shape your product's design within the platform's existing

architecture, including:

o How your product fits the multi-agent orchestration model

o Use of shared retrieval (RAG) and knowledge services

o Reuse of platform skills, tools, and components

o Model selection within the platform's model orchestration layer

o Conversational and generative UI surfaces

Apply and help refine shared platform standards for:

o Prompt and agent design patterns

o Evaluation, quality, and reliability

o Responsible AI, safety, and governance

o Human-in-the-loop and escalation patterns

Make product-level decisions on feature flags, progressive rollout, and how your product respects

the tenant isolation, data boundaries, and security patterns set by the platform.

Delivery Enablement

Work closely with delivery teams to:

o Understand real-world use cases and jobs to be done

o Uncover friction in how people currently get work done

o Define how your product slots into existing workflows

Shape how users interact with your product its assistants, reusable skills and workflows, shared

knowledge, and configurable patterns they can tailor to their context.

Execution & Delivery

Collaborate with Engineering to:

o Prioritize the backlog and sequence investments

o Drive latency, quality, and cost improvements for your product

o Improve scalability, reliability, and observability

Define success metrics and contribute to evaluation and monitoring, both offline and online,

including usage and cost models for your product's AI workloads.

Own documentation and standards for your product, aligned to platform conventions.

Stakeholder Management

Partner across Engineering, Design, Security & Compliance, fellow product managers, and the

practitioner community.

Communicate trade-offs and translate fluently between technical teams and business stakeholders.

Present your product's roadmap, progress, and impact to platform leadership.

What You'll Bring

Proven PM experience shipping technical or platform products; experience with AI/ML or data

intensive products strongly preferred.

Working fluency in modern AI (LLMs, agents, RAG, evaluation) and the trade-offs between them. A

track record of translating ambiguous problems into clear roadmaps and shipped outcomes.

Strong collaboration across engineering and design, and the ability to influence without authority.

Comfort operating within enterprise responsible-AI, security, and governance constraints.

Nice to Have

Experience with professional services / consulting delivery models.

Hands-on familiarity with AI tooling and agent frameworks.

Background designing for both practitioner and leadership audiences.