Principal Product Manager, Agentic AI

OpenKrill; United States; À distance; USD 170 000 - 210 000; Plein temps

Résumé du poste

Legion is building AI agents that handle workforce management tasks with a human-in-the-loop where necessary. This Principal Product Manager role leads specification, prototyping, evaluation, and enterprise deployment of agentic AI solutions in a remote US setting.

You will work closely with Legion's AI Lab engineers and the CEO to shape the product from concept through launch, balancing technical feasibility with business impact.

Qualifications

• 7+ years in product management (Senior) or 10+ years (Principal), incl. 2+ years shipping external, paying users — not internal tools, proofs of concept, or hackathon projects
• Demonstrated hands-on capability; expect to show your work
• Substantial enterprise B2B SaaS experience: multi-tenant, RBAC, audit trails, SSO, data residency, security review, enterprise procurement

Responsabilités

• Agent definition: decide responsibilities, tools, escalation, and manager view; pre-launch shaping.
• Problem selection: identify workflows for agent value in operations like district/store managers and frontline employees.
• The human-in-the-loop contract: define autonomy, escalation thresholds, overrides, logging, audits.
• Build quality: stay engaged through implementation, test against spec, and ensure usable products.
• Evaluation: create test sets, review failures, set pass criteria, hold release when needed.
• Specifications: write FRDs as ground-truth specs; maintain accuracy post-release.
• Commercialization: packaging, pricing inputs, field readiness; address monetization questions.
• Design-partner delivery: lead enterprise deployments, pilot scoping, success criteria, reviews.

Connaissances

Senior product management

Principal product management

Shipping LLM features

Enterprise SaaS

Code reading / engineering mindset

Strong written communication

Outils

Claude Code

Agentic coding tools

Description du poste

Principal Product Manager, Agentic AI

REMOTE - US

JOB OVERVIEW

Legion is building AI agents that carry out workforce management work, not agents that describe it.

Much of what is presented as AI in workforce management today is a conversational layer over an existing scheduling screen. The more substantial problem — software that reasons over demand, labor regulation, and employee preferences, then acts, with a person in the loop wherever the stakes require one — remains unsolved across the category. Legion intends to solve it, and this role leads the product work to get there.

You will take an agent surface from operational research through specification, prototype, evaluation, and enterprise deployment, working hands-on throughout. You will work closely with Legion's AI Lab engineers and with our CEO, who is engaged in this work daily.

RESPONSIBILITIES AND DUTIES

Agent definition. Determine what an agent is responsible for, which tools it may call, what it must elevate, what it must never do, and what a manager sees when it acts. These decisions affect employee pay and legal compliance. Much of this work is pre-launch, so you will shape the direction rather than inherit it.

Problem selection. The hardest judgement in agentic product work is identifying which workflows suit an agent and which do not. You will spend substantial time inside customer operations — district managers, store managers, labor operations, frontline employees — and return with a ranked, defensible view of where an agent creates measurable value and where it does not.

The human-in-the-loop contract. Scheduling decisions affect income, childcare arrangements, and legal protections. For each agent action you will define where autonomy is appropriate, the escalation thresholds, how a manager overrides the system, what is logged, and how a compliance team audits the outcome. Legion treats this as a first-class design problem.

Build quality. You are accountable for what ships, not for what was specified. That means staying engaged through implementation: reviewing the build against the specification as it comes together, using the product yourself before anyone else is asked to, joining bug triage and design review, catching drift from intent while it is still cheap to correct, and holding a release when the quality is not there. Our customers experience the build, not the document. A well-written specification that produced a product managers do not trust is failed product work, and we assess it that way.

Evaluation. Build and maintain the evaluation sets that determine whether an agent is ready to ship and whether it is degrading in production — writing the cases, reviewing failure traces, setting the pass criteria. You will be the person who holds a release when the demonstration is persuasive and the evaluation is not.

Specifications. Legion uses Functional Requirements Documents as its ground-truth specification layer; release notes, enablement material, and sales content are generated from maintained specifications rather than reconstructed from code or institutional memory. Write FRDs that meet that standard and keep them accurate after release. A specification is where the work begins, not where your responsibility ends.

Commercialization. Partner with go-to-market on packaging, metering, pricing inputs, and field readiness. Agentic products raise difficult commercial questions — what unit to meter, what a customer is paying for when software performs work a person previously did — and you will be expected to hold a considered position on them.

Design-partner delivery. Lead agent deployments with large enterprise design partners: scoping the pilot, establishing success criteria before it begins, joining failure reviews, and determining what becomes generally available and what remains bespoke.

TECHNICAL EXPECTATIONS

• Writing and iterating on prompts and system prompts directly, and explaining why a change improved a behavior
• Standing up a working prototype of a proposed agent — tool definitions, structured outputs, retries, guardrails — before engineering is asked to build anything
• Using agentic coding tools (Claude Code or equivalent) daily: prototyping, data analysis, specification work, and reading the codebase behind your specifications
• Reviewing an execution trace and diagnosing whether a failure originates in a tool description, a retrieval gap, a context-window constraint, an under-specified output schema, or a genuine model limitation
• Building and running evaluation sets, and interpreting the results without a data scientist to translate them
• Holding an informed position on retrieval design, tool-use patterns, structured output, model routing, latency and cost trade-offs, and multi-tenant safety in enterprise SaaS
• Opening pull requests against our codebase — a prototype, a prompt change, an evaluation fixture, a specification stub. Pushing code is permitted and actively encouraged for product managers who want to work that way. It is not a requirement of the role, and the same review standards apply to everyone.
• Following model releases, agent frameworks, and protocol changes, and translating them into roadmap implications within days rather than quarters

We recognize that this is a demanding technical bar for a product role. It reflects what the work actually requires.

REQUIRED SKILLS AND QUALIFICATIONS

• 7+ years in product management (Senior) or 10+ years (Principal), including at least 2 years shipping LLM-backed features to external, paying users — not internal tools, proofs of concept, or hackathon projects
• Demonstrated hands-on capability of the kind described above. Expect to show your work.
• Substantial enterprise B2B SaaS experience: multi-tenant architecture, role-based access control, audit trails, single sign-on, data residency, security review, and enterprise procurement. You should be able to anticipate what a customer's security questionnaire will ask about an LLM feature.
• A record of taking an ambiguous, operationally complex domain problem through to a shipped product with demonstrated adoption — not only launch
• Evidence of staying with a product through implementation and owning its quality at release. We will ask about a release you delayed or stopped, what you found, and how you found it.
• The judgment to recognize what current models cannot do reliably, and the willingness to say so when a room expects a different answer
• Comfort working inside a codebase. An engineering background is not required, but you should be able to read the code behind what you are specifying, run it locally, and open a pull request.
• Strong written communication. Legion runs on documents, and candidates are evaluated substantially on how they write.

PREFERRED QUALIFICATIONS AND ATTRIBUTES

• Direct experience in workforce management, HCM, ERP, supply chain, or another operationally complex enterprise domain with compliance exposure
• Familiarity with labor compliance regimes — predictive scheduling ordinances, meal and rest break rules, union work rules — or demonstrated ability to become fluent in a comparable regulatory domain quickly
• Background in demand forecasting, optimization, or decision systems
• Experience building platform or authoring surfaces that customers configure themselves
• An engineering or technical degree, or prior experience as an engineer or data scientist
• Experience with products sold into frontline-heavy industries: retail, hospitality, healthcare, or manufacturing

SCOPE OF ROLE, STATED PLAINLY

So that candidates can assess fit early:

• This is an individual contributor role. There is no team to inherit and no management responsibility at present.
• The role is accountable for what ships, not for what is specified. Writing the specific