Senior Product Manager

Description

We are looking for a skilled and passionate Senior Product Manager to join our Agent Builder team in our NYC office. Working in a high-growth scale-up, you’ll have the unique opportunity to drive innovation at scale while contributing to a company transforming the world of audit and finance.

About the role:

Agent Builder is where firms turn their own methodology and knowledge into AI agents, and manage those agents for their whole life, from definition to retirement. A generic agent knows nothing about how a firm works. Its methodology, guidance, templates and past work are what make an agent behave like one of the firm's own people, and that knowledge and context layer is what sets our agents apart.

You'll own the Agent Builder product in Alwin, our agentic platform for audit and finance. That means the definition model behind an agent, how firms test and publish agents they can trust, and how those agents are versioned, governed and retired at scale. There's no playbook for letting professionals build, run and govern production agents in a regulated domain. You'll help write it.

This is a product role with a strong technical bent, and we welcome candidates from product or technical PM backgrounds. You'll work closely with a new engineering team in New York, with our Agent Runtime, Evals and Document Intelligence teams in Amsterdam, and with the customers and domain experts who define what a good agent looks like.

Why the company?

The hardest AI problems aren't about getting an answer fast. They're about getting one people can trust.

We believe trust is essential to how society works. Every day, we rely on information that's been independently verified, from financial statements to the products we buy, to the sustainability claims companies make. Behind that trust are audit and finance professionals who examine evidence, verify information and hold organizations accountable.

At the company, we build AI solutions that empower those professionals. By making evidence easier to trace and verify, we help them focus on the judgment and expertise that make their work so important.

For our product and engineering team, this is where things get exciting. In audit, a plausible answer isn't good enough. Every output has to be explainable and traceable back to its source. We've just launched a brand new agentic platform, Alwin by the company. AI agents running in the cloud across hundreds of documents at once, reasoning across PDF, Word and Excel, with a full audit trail behind every step. There are lots of greenfield problems ahead, and real room to shape what we build and how we build it.

And we build at scale.

If you want to build AI that professionals trust with work that really matters, this is your chance to help shape the future of audit and finance and do the best work of your career along the way.

What you'll own

Knowledge and context: how a firm's methodology, guidance, templates and prior work are captured, kept current, permissioned and served to agents at the moment they need them

Definition: the authoring experience and the definition model that turn a procedure into a multi-step agent: steps, tools, knowledge sources, thresholds and human-in-the-loop gates

Test and publish: how builders evaluate an agent against real cases before it ships, and the review and approval it passes before it reaches the Agent Library

Run and observe: tracing what an agent did on a live engagement, which knowledge it used and why

Govern, version and retire: permissions, change history, approvals, rolling out new versions without breaking work in progress, and retiring agents and outdated knowledge cleanly

Responsibilities

• Set the product direction and roadmap for Agent Builder, and make the trade-offs explicit
• Get the abstraction right: decide how much power builders get and where the guardrails sit, so agents are flexible enough to encode real methodology and consistent enough to trust
• Define how we prove an agent works, with evals on real cases and results reported with a stated method, not a headline accuracy number
• Treat lifecycle as product: versioning, approvals, rollout and retirement designed in from day one
• Keep confidentiality central: what an agent learns from one customer's work must never reach another
• Prototype flows yourself before writing specs, then write short specs and decision memos engineers can build from
• Work with forward-deployed engineers, solutions architects and go-to-market teams, and turn what they learn on enterprise rollouts into platform capabilities rather than one-offs
• Run discovery with the people who govern agents at customer firms, and change the plan when the evidence says so
• What You Bring:
• Experience as a PM on platform or developer-facing products that other people build on, from a product or a technical PM background
• Experience shipping LLM or agent features in production. You understand context and retrieval, tool calling, evals, human review in the loop and non-determinism, and why "it worked in the demo" isn't evidence
• Comfort with data models and system design: definition schemas, versions, immutable records, permissions, and how objects relate to each other
• Experience with products that have a lifecycle customers depend on: versioning, permissions, release and deprecation
• Enough technical depth to make architecture trade-offs with engineering leads and challenge designs credibly
• Discovery discipline: you separate what customers said from what you hoped they said
• Hands-on use of AI tools in your own work, including prototyping
• Clear, concise writing
• What sets you apart
• Knowledge of audit or accounting, from practice or from building products for auditors, finance teams or other expert users in regulated fields
• Familiarity with how firms validate and govern tools before relying on them, and with quality management standards such as ISQM 1
• Experience with knowledge management or enterprise search: ingestion, retrieval, document-level permissions, keeping content fresh
• Experience with low-code or configuration products, workflow designers, rule engines or DSLs
• Agent evaluation in practice: datasets, offline and online experiments, failure classification, measuring the effect of context on quality
• Running design-partner programmes with large enterprise customers

Benefits

• Equity (Stock Appreciation Rights) to share the company's success and growth
• Flexible paid time off
• Comprehensive medical and dental coverage
• 401K match
• Paid parental leave
• Company-sponsored lunch
• Hybrid mode of work (at least 3 days onsite in our New York City office)
• Access to continuous learning and development initiatives to grow your skills
• Engage with a vibrant international team spread across five global offices
• Access to the mental health and wellness platform
• Multiple social activities for team building, including company-wide events like the company GO, where global teams come together
• Recruitment Process
• Recruitment Screen (30 minutes, online)
• Hiring Manager Interview (30 minutes, online)
• Panel Presentation (60 minutes, online)
• Leadership interview (30 minutes, on-site)
• Final Interview (30 minutes, online)
• At the company, we believe great ideas come from different perspectives, experiences, and backgrounds. We are committed to building an inclusive workplace and providing every candidate with a fair and equitable hiring experience.
• Our hiring decisions are merit-based and grounded in the skills, experience, potential, and capabilities relevant to the role.
• We welcome applicants regardless of age, race, ethnicity, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or any other characteristic that makes you, you.
• What matters to us is what you can bring to the role, the team, and the company.