Senior Product Manager, AI

Description

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.

Requirements

Required Qualifications

• •7+ years of product management experience in B2B SaaS, workforce technology, compliance software, transportation technology, enterprise workflow software, data products, automation products, or a related domain.
• •1 to 2+ years shipping AI/ML, generative AI, intelligent automation, conversational analytics, agentic workflows, or data-driven workflow products to real users.
• •Ownership of at least one AI-powered or agent-based capability, such as a co-pilot, assistant, intelligent automation, multi-step workflow, conversational interface, recommendation system, or AI-assisted workflow.
• •Strong technical fluency in LLMs, prompt design, context engineering, retrieval-augmented generation, structured outputs, agent frameworks, evaluation methods, and cost, latency, quality, and safety tradeoffs.
• •Experience defining product strategy, requirements, user stories, acceptance criteria, success metrics, rollout plans, and post-launch iterations.
• •Strong product judgment, including the ability to decide when AI is appropriate and when deterministic workflow automation is better.
• •Experience with sensitive data, regulated workflows, compliance-heavy products, high-trust customer environments, or enterprise systems.
• •Strong analytical, written, and verbal communication skills.
• •Bachelor’s degree in business, computer science, engineering, data science, human-computer interaction, or a related field. Equivalent experience will also be considered.

Preferred Qualifications

• •Experience with employment screening, drug screening, employment verification, credentialing, license or certification tracking, continuous monitoring, audit readiness, regulated operations, document management, healthcare, transportation, employee life cycle, or compliance-heavy workflows.
• •Experience launching AI-powered features, agents, copilots, workflow automation, recommendation systems, or data-driven decision support into production.
• •Exp