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 such as golden datasets, offline evals, human review, LLM-as-judge where appropriate, regression checks, shadow mode, staged rollouts, and online experiments.

What Success Looks Like in the First 6 to 12 Months

• A clear AI product roadmap is defined, prioritized, and aligned with company strategy.
• High-value AI and automation opportunities are translated into MVPs, measurable outcomes, and risk-managed launch plans.
• At least one AI-enabled capability, intelligent workflow, data-driven insight, or integration-driven experience is launched, piloted, or meaningfully advanced toward production.
• Customer evidence from interviews, usage data, support trends, or adoption metrics shows that shipped capabilities are solving real workflow problems.
• A practical evaluation approach is established so AI quality, safety, and regression risk are visible to the team.
• AI product practices improve across guardrails, release readiness, evaluation, human-in-the-loop design, observability, and post-launch monitoring.