Artificial Intelligence Product Lead

AI Product Lead Job Description Department: Data Technology Job Status: Full-Time FLSA Status: Salary-Exempt Reports To: AI/ML Engineering Manager Location: The Woodlands, TX Amount of Travel Required: Less than 5% Work Schedule: Monday - Friday, 8 a.m. – 5 p.m. Positions Supervised: n/a AIP: Level 6 POSITION SUMMARY: The AI Product Lead drives how AI delivers value across the organization. You will discover high-impact opportunities, maintain a clear and prioritized backlog, and empower teams to adopt AI with confidence. This role owns the AI intake, discovery, and enablement operating model; the AI/ML Engineering Manager owns the final roadmap and delivery priorities. Your mission is enablement: equipping people across every department with the tools, examples, and coaching they need to solve problems independently— raising AI literacy and accelerating results organization-wide. You will identify patterns across teams, consolidate recurring needs into reusable solutions, and apply rigorous judgment to ensure the engineering team invests in initiatives that demand specialized development, integration, or production-grade reliability. For initiatives that warrant engineering, you will define scope, success criteria, and stakeholder expectations, then establish a structured handoff with clear milestones and decision points for the engineering team. This model enables engineering to focus on high-impact technical challenges while empowering the organization with speed, AI literacy, and measurable outcomes. ESSENTIAL FUNCTIONS: (The following duties and responsibilities are all essential job functions, as defined by the ADA, except for those that begin with the word "may.") Organization-wide AI enablement Foster trusted relationships with teams across every department; equip them with the tools, training, and support to use AI confidently and independently. Create and maintain org-visible example project spaces with curated prompts, guardrails, and edge-case handling, and example workflow automations with sample files that teams can copy and adapt. Partner with the AI/ML Engineering Manager to deliver office hours, workshops, and coaching; may lead training sessions as needed. Identify when teams need additional support or when a use case warrants escalation to engineering for custom development. Proactively address adoption barriers (e.g., fear of job displacement, skepticism, workflow disruption) through clear communication, success stories, and tailored change management strategies. Opportunity discovery and demand intake Meet with departments and sub-teams to surface high-value AI opportunities, clarify goals, and translate requests into structured problem briefs with measurable outcomes, constraints, and explicit non-goals. Operate a single intake funnel with a consistent scoring rubric; maintain a transparent, stack-ranked backlog in Azure DevOps. Distinguish between Field/Controls Engineering needs (e.g., Predictive Maintenance, knowledge-enabled field assistants) and Corporate/Internal needs (broad adoption vs. niche cohort solutions); apply appropriate success criteria and capacity guardrails. Build and maintain a stakeholder map (IT, Legal, Compliance, HR, Finance, Operations) to ensure alignment on governance, data access, and cross-functional dependencies, coordinating with the Data Governance & Delivery Manager and the Principal Architects (Data Platforms and Applications Engineering); this role does not set governance policy. Backlog clarity and initiative progression Clarify what stakeholders expect at each phase (Discovery Prototype Pilot Scale) and document requirements, presenting evidence and recommendations, with the AI/ML Engineering Manager having final /no-go authority. Ensure every backlog item has a complete problem brief, success metrics, and defined non-goals before engineering work begins. Regularly review and recommend reprioritization of the backlog based on business value, urgency, and strategic alignment, in coordination with the AI/ML Engineering Manager. Pattern recognition and reuse Identify recurring solution patterns across departments. When materially similar solutions emerge, recommend consolidation into reusable blueprints (project spaces, prompt libraries, evaluation harnesses) or platform capabilities to reduce duplication and increase leverage. Apply the do-it-twice rule: the second time a pattern appears, extract a reusable blueprint; the third time, propose a durable platform investment. Maintain a library of reusable components and ensure teams know how to discover and adopt them. Communication and adoption Publish concise monthly updates on portfolio progress, including wins, adoption/ROI, costs, reliability, and sunsets. Facilitate showcases, project demos, feedback sessions, and channel insights back into the backlog and enablement materials. Keep executives and stakeholders aligned on trade-offs, priorities, and delivery timelines. Establish feedback loops (surveys, usage telemetry, stakeholder interviews) to measure enablement effectiveness and iterate on training materials, project spaces, and governance standards. Success Measures Enablement impact: percentage of requests resolved via self-serve project spaces; adoption of demo spaces and departmental blueprints; time-to-first-value for new teams. Portfolio outcomes: Field initiatives graduating to scale with measured ROI (e.g., downtime avoided, MTBF/MTTR); Corporate impact (adoption, time saved, decision quality); healthy continue/pause/stop rate at early phases. Clarity and discipline: percentage of backlog items with complete problem briefs and success metrics; cycle time from intake to decision; stakeholder satisfaction with backlog transparency. Reuse and leverage: increase in shared blueprints/components adopted across departments; reduction in duplicative efforts; growth in reusable pattern library. Stakeholder satisfaction: NPS or satisfaction scores from departments, executives, and the engineering team on enablement quality, backlog clarity, and responsiveness. Performs other related duties as assigned to assist with successful operations and business continuity. POSITION REQUIREMENTS: Successfully passes all applicable general pre-employment testing including but not limited to: background check, pre-employment drug screening, pre-employment fit tests, pre-employment aptitude and/or competency assessment(s). Proficiency in the spoken English language Position requires in-person, predictable attendance EDUCATION/EXPERIENCE LEVEL: 6+ years in Product, Strategy, Analytics, or a related field; 3+ years delivering AI/ML or LLM-powered products, platforms, or enablement programs. Proven track record in building enablement toolkits, training programs, or org-visible demos that non-technical users adopted successfully. Strong understanding of ML/LLM capabilities, limitations, and cost/latency considerations. Excellent communication, facilitation, and stakeholder management skills; disciplined with prioritization, time-boxing, and scope control. Bachelor's degree or equivalent practical experience. QUALIFICATIONS, SKILLS, COMPETENCIES, AND ABILITIES: Hands-on experience with Abacus.AI, Databricks, chatbot development, vector search/RAG, or prompt engineering and safety guardrails. Comfortable reading Python and SQL to understand technical context and communicate effectively with engineering. Experience designing and measuring AI adoption programs (e.g., roadshows, office hours, newsletters, usage dashboards). PHYSICAL REQUIREMENTS/WORK ENVIRONMENT: The physical demands and work environment described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Frequently required to walk, sit, climb, bend, reach, and squat/kneel. The AI Product Lead works primarily indoors and will be sitting for prolonged periods of sitting at a desk and working on a computer. Must be able to access and navigate each department at the organization’s facilities. AI Product Lead may be required to lift heavy objects; therefore, the AI Product Lead must be able to lift 25lbs. Work hours may include early morning, late afternoon/evening hours, and weekends in combination, depending on job demands. AAP /EEO STATEMENT: The Company is committed to the cause of equal employment opportunity for all employees and applicants, thus abiding by all applicable state and federal laws. Our practices regarding employment, job promotion, compensation, training, and termination do not discriminate based on race, color, religious creed, age, sex, national origin, veteran's status, disability, pregnancy, genetic information, or any other legally protected status. It is expected that all employees, both management and staff, will fully support these nondiscriminatory policies. The company has reviewed this job description to ensure essential functions and duties have been included. It is not intended to be an exhaustive list of all functions, responsibilities, skills, and abilities. Last Revised 01/2026.