Artificial Intelligence Product Lead

Beusa Energy, LLC•The Woodlands, TX

4d•Onsite

About The Position

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.

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
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.

Nice To Haves

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).

Responsibilities

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.
Performs other related duties as assigned to assist with successful operations and business continuity.