Senior Product Manager, AI

Job Title: Senior Product Manager

Department: Product

Reports To: Chief Product Officer

Employment Type: Full-Time, Salary-Exempt, Remote

Salary: $110,000-$140,000

About AccuSourceHR: AccuSourceHR is a full-service employment screening organization headquartered in Phoenix, Arizona. Since 1999, we have helped employers make faster, safer, and more confident workforce decisions through reliable screening technology, high-quality client care, and PBSA-accredited practices. We are investing in modern product experiences, integrations, automation, data-driven insights, and practical AI capabilities that help customers operate more efficiently and with greater confidence.

Position Overview

As a Senior Product Manager, you'll lead the end-to-end product life cycle, from ideation to launch and iteration. Collaborating with cross-functional teams, including engineering, UX/UI, operations, implementation, and marketing, you'll develop scalable, user-centric solutions that align with our mission to enhance workforce screening services. What you will own:

Product Strategy and Roadmap

• Own product strategy and roadmap for AI-enabled capabilities, workflow automation, data-driven insights, integrations, and platform improvements.

• Translate customer problems, business goals, technical possibilities, and operational constraints into clear product priorities.

• Define MVPs, phased releases, success metrics, rollout plans, and risk controls.

• Build business cases tied to revenue, retention, efficiency, risk reduction, or competitive differentiation.

• Decide where AI creates durable value and where rules-based workflow or deterministic automation is the better answer.

AI Product Development

• Partner with engineering to design, test, and launch AI-enabled features, copilots, assistants, agents, conversational experiences, and intelligent workflows.

• Define user journeys, permissions, tool interactions, guardrails, fallback logic, escalation paths, confidence thresholds, and human review requirements.

• Write clear requirements involving prompts, context engineering, structured outputs, retrieval, workflow orchestration, model limitations, evaluation criteria, and release readiness.

• Design natural-language experiences that help users ask questions over complex data and receive structured, defensible answers such as summaries, tables, explanations, or recommended next steps.

• Define release-readiness criteria for AI features, including hallucination handling, PII protection, audit trails, observability, rollback plans, and user transparency.

• Use modern AI tools in your own PM workflow for research, synthesis, requirements, analysis, and prototyping.

Workflow and Domain Leadership

• Develop deep understanding of customer workflows across screening, compliance operations, document management, monitoring, business-system integrations, and regulated operational environments.

• Identify opportunities to connect fragmented workflows, data sources, partner systems, and customer operations into clearer product experiences.

• Frame integration depth, data contracts, permissions, data quality, and build-versus-partner tradeoffs as product decisions.

• Partner with customers and internal teams to understand real workflows, edge cases, compliance requirements, operational pain points, and buyer priorities.

Discovery and Execution

• Conduct customer discovery, stakeholder interviews, workflow analysis, competitive research, and market assessment.

• Shadow or interview users who operate complex workflows so product decisions are grounded in real operator behavior, not assumptions.

• Use customer feedback, support trends, usage data, sales input, implementation friction, and market signals to inform priorities.

• Track foundation model releases, agent frameworks, AI evaluation methods, and relevant workflow automation trends.

• Define personas, jobs-to-be-done, user journeys, problem statements, product hypotheses, and adoption risks.

• Lead cross-functional execution across engineering, UX, operations, implementation, compliance, customer success, sales, and marketing.

Responsible AI and Measurement

• Partner with engineering, security, compliance, legal, and operations to manage risks related to sensitive data, PII, bias, explainability, auditability, and human oversight.

• Define when AI should recommend, summarize, classify, draft, automate, escalate, or stay out of the workflow.

• Establish product-level AI governance practices, including evaluation criteria, monitoring, documentation, user transparency, audit trails, and release controls.

• Define KPIs for adoption, workflow completion, time savings, quality, customer satisfaction, risk reduction, operational efficiency, and revenue impact.

• Define AI-specific metrics such as task success rate, acceptance rate, escalation rate, hallucination rate, user trust, cost per successful outcome, and time saved.

• Use evaluation approaches…