Senior Product Manager, Agent Builder

DataSnipper•New York, NY

1d•$195,000 - $220,000•Hybrid

About The Position

DataSnipper is hiring a Senior Product Manager to own Agent Builder, a product within Alwin, its agentic platform for audit and finance. Agent Builder enables firms to turn their own methodology, guidance, templates, and prior work into AI agents and manage those agents throughout their lifecycle, from definition to retirement. The role will shape how firms create, test, publish, observe, govern, version, and retire trusted production agents in a regulated domain. This is a technically oriented product role, open to candidates from product or technical product management backgrounds. The Senior Product Manager will work closely with a new engineering team in New York, Agent Runtime, Evals, and Document Intelligence teams in Amsterdam, as well as customers and domain experts. The role calls for setting product direction, balancing builder flexibility with appropriate guardrails, designing credible evaluation and lifecycle processes, and ensuring customer confidentiality. The position is hybrid, with at least three days onsite each week in DataSnipper's New York City office.

Requirements

Experience as a product manager on platform or developer-facing products that other people build on, from either a product or technical product management background.
Experience shipping LLM or agent features in production.
Understanding of context and retrieval, tool calling, evaluations, human review in the loop, and non-determinism, including why a successful demo is not evidence of production effectiveness.
Comfort with data models and system design, including definition schemas, versions, immutable records, permissions, and relationships between objects.
Experience with products whose lifecycle customers depend on, including versioning, permissions, releases, and deprecation.
Sufficient technical depth to make architecture trade-offs with engineering leads and credibly challenge designs.
Disciplined customer discovery, separating what customers said from what the product team hoped they said.
Hands-on use of AI tools in personal work, including prototyping.
Clear, concise writing.

Nice To Haves

Knowledge of audit or accounting, through practice or 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, including ingestion, retrieval, document-level permissions, and keeping content fresh.
Experience with low-code or configuration products, workflow designers, rule engines, or DSLs.
Practical agent evaluation experience, including datasets, offline and online experiments, failure classification, and measuring the effect of context on quality.
Experience running design-partner programmes with large enterprise customers.

Responsibilities

Set the product direction and roadmap for Agent Builder and make product trade-offs explicit.
Define how much flexibility builders have and where guardrails should sit, so agents can encode real methodology while remaining consistent and trustworthy.
Define how agents are evaluated on real cases, with results reported using a stated method rather than a headline accuracy figure.
Design lifecycle capabilities—including versioning, approvals, rollout, and retirement—from the outset.
Keep customer confidentiality central and ensure knowledge from one customer's work is not made available to another.
Prototype product flows before writing specifications, then produce concise specifications and decision memos engineers can build from.
Work with forward-deployed engineers, solutions architects, and go-to-market teams to turn enterprise rollout learnings into platform capabilities rather than one-off solutions.
Conduct discovery with people who govern agents at customer firms and adjust the product plan based on evidence.
Own how firm methodology, guidance, templates, and prior work are captured, kept current, permissioned, and provided to agents when needed.
Own the agent authoring experience and definition model, including steps, tools, knowledge sources, thresholds, and human-in-the-loop gates.
Own builder evaluation and review and approval processes before agents are published to the Agent Library.
Enable tracing of what an agent did on a live engagement, including which knowledge it used and why.
Define permissions, change history, approvals, version rollouts that do not disrupt work in progress, and clean retirement of agents and outdated knowledge.