Product Manager, AI Platform

Bioworld Inc•Irving, TX

2d•Hybrid

About The Position

The Product Manager, AI Platform will help deliver Bioworld’s enterprise AI platform, including applications, data services, and intelligent agents that turn business questions into measurable results. This role translates business needs into clear product requirements, prioritizes the backlog, makes informed trade-offs, and partners closely with engineering to bring solutions into production. The Product Manager owns delivery from intake through release, ensuring features ship, work as intended, and create measurable business value. The role partners with distributed and offshore engineering teams and requires clear communication across time zones. The Product Manager will write simple, self-contained requirements, define clear acceptance criteria, and maintain organized handoffs while working primarily during standard U.S. business hours.

Requirements

3-5 years of experience in product management, product ownership, or a blended product/analyst role, ideally supporting data, analytics, or platform products.
Demonstrated ability to translate business needs into clear, buildable, prioritized specifications.
Proven experience partnering with engineers to ship products or features that delivered measurable value.
Comfort working with data, including basic SQL or similar tools for profiling and validating assumptions.
Excellent stakeholder management and communication skills across business and technical audiences.
Demonstrated adoption/change-management experience with user populations that may be reluctant to adopt new technology.
Experience using Jira or similar project-management software to manage backlogs, user stories, priorities, and delivery status.

Nice To Haves

Practical experience with frontier LLM platforms such as Anthropic Claude, OpenAI, Gemini, or Grok, including prompting and model selection.
Understanding of row-level security, identity-scoped data access, and contractual or intellectual-property constraints around data.
Comfort using AI-assisted development tools to prototype ideas and validate concepts before engineering investment.
Exposure to AI/ML-enabled products, LLM-powered applications, or intelligent agents.
Familiarity with modern cloud data platforms such as Microsoft Fabric/Azure or equivalent lakehouse and semantic-layer environments.
Understanding of API-driven architecture and how front-end applications consume services.
Exposure to sales intelligence, licensing/royalties, enterprise operations, or similar business domains.
Experience working with offshore or distributed engineering teams across time zones.

Responsibilities

Partner with business stakeholders to elicit, challenge, and document requirements for AI-driven features, analytics views, and platform services.
Translate vague or evolving business requests into clear problem statements, prioritized user stories, and acceptance criteria.
Own and groom the product backlog so each item has a stated business outcome and measurable success criteria.
Perform light data profiling to validate assumptions before development begins.
Own delivery end to end, including scope, sequencing, prioritization, trade-offs, and release readiness.
Partner closely with engineers to clarify requirements, unblock decisions, review approaches, and adjust scope as technical realities emerge.
Partner effectively with offshore engineering teams through clear specifications, disciplined async handoffs, and shared delivery visibility.
Coordinate stakeholder readiness, rollout plans, and fallback plans to support predictable releases.
Serve as the primary point of contact between the Data & AI Platform team and business domains such as Sales, Finance, Licensing, Product, and Operations.
Build trusted relationships with domain owners and subject-matter experts by understanding goals, constraints, and definitions of success.
Manage expectations, communicate roadmap and trade-offs, and negotiate scope and priority across competing requests.
Own business intake from initial idea through delivered value.
Define what “done” means through clear acceptance criteria and edge-case expectations.
Validate that shipped features meet acceptance criteria and that analytical outputs and AI/agent responses are correct against source-of-truth data, in partnership with Engineering.
After releases, measure adoption, accuracy, time savings, or business impact and apply those insights to future increments.
Maintain a healthy, well-prioritized backlog with transparent ownership and delivery status.
Establish a steady cadence of small, measurable product increments that demonstrate business value.
Help drive adoption and change management, particularly where users may be reluctant to adopt new tools or workflows.