AI Lead

Flywheel Energy is seeking a visionary and hands-on AI Lead to drive the adoption, development, and strategic expansion of artificial intelligence across the organization. As a ~1,000-person upstream oil and gas company with systems in an early stage of maturity, we are at an inflection point - and this role is central to how we evolve. 

The AI Lead will serve as Flywheel's internal champion for AI, making the case for intelligent automation at every level of the business, from the field to the executive suite. You will build and lead a team of AI engineers focused on delivering real business automation use cases - not just pilots - that eliminate manual work, accelerate decision-making, and create durable operational advantages. You will also own the company's AI strategy end to end: developing the roadmap, prioritizing investments, evaluating platforms, and ensuring the organization has the capabilities and culture to scale AI responsibly over time. 

This is a rare opportunity to define what AI looks like at Flywheel from the ground up. 

Key Responsibilities:

1. AI Evangelism and Culture Building 

Serve as the face of AI at Flywheel. Build enthusiasm, trust, and understanding of AI across all departments - operations, land, finance, HSE, and back-office functions. Develop and deliver internal communications, lunch-and-learns, demonstrations, and executive briefings that make AI tangible and accessible to non-technical stakeholders. Identify and celebrate early wins to build organizational momentum and reduce resistance to change. 

1. AI Strategy and Roadmap 

Develop and maintain Flywheel's enterprise AI strategy, including a prioritized roadmap of use cases, platform investments, and capability-building initiatives. Align the AI roadmap with business priorities across exploration, production, and corporate functions. Establish a framework for evaluating and selecting AI tools, vendors, and platforms that balances near-term impact with long-term architectural coherence. Present strategy and progress to executive leadership on a regular cadence. 

1. Team Leadership 

Build, lead, and develop a team of AI engineers responsible for designing and delivering business automation solutions. Set clear expectations, foster a culture of rigor and experimentation, and ensure the team is working on problems that matter. Attract and retain strong technical talent. Provide technical mentorship and career development support across the team. 

1. Business Automation Use Case Delivery 

Lead the end-to-end delivery of high-impact AI automation use cases across the enterprise. Prioritize areas such as production reporting, land and lease management, AFE processing, procurement, field operations, and financial close. Drive projects from problem definition through deployment and adoption, working closely with business stakeholders to ensure solutions are usable, trusted, and sustained over time. Leverage large language models (LLMs), agentic AI workflows, and other relevant technologies as appropriate. 

1. Platform and Technology Evaluation 

Stay current with the rapidly evolving AI landscape and identify emerging technologies that are relevant to Flywheel's context. Evaluate AI platforms, foundation model providers, automation frameworks, and tooling with a practical lens - what actually works in an upstream energy environment. Recommend and govern the AI technology stack in partnership with the Enterprise Architect and IT leadership. 

1. Responsible AI and Governance 

Establish guardrails and governance practices for responsible AI use at Flywheel. Define standards around data privacy, model reliability, human oversight, and auditability. Ensure that AI systems deployed in production are monitored, maintained, and improved over time. Work with legal, compliance, and cybersecurity to manage risk appropriately. 

1. Data and Integration Enablement 

Partner with data and software teams to ensure AI use cases are grounded in reliable, accessible data. Identify data gaps that limit AI effectiveness and advocate for the investments needed to close them. Collaborate with the Enterprise Architect to align AI development with the broader data platform and integration strategy. 

Required Qualifications: 

1. 8+ years of progressive experience in AI, machine learning, data science, or a related technical field, with at least 2 years in a leadership or people management role. 

1. Demonstrated experience building and deploying AI or automation solutions in a business context - not just research or proof-of-concept work. 

1. Hands-on experience with large language models (LLMs), agentic AI frameworks, or similar generative AI technologies. 

1. Proven ability to communicate AI concepts clearly to non-technical audiences, including executives and frontline business users. 

1. Track record of influencing organizational behavior and driving adoption of new tools or ways of working. 

1. Strong project delivery skills - able to move from strategy to execution and hold a team accountable to outcomes. 

1. Experience working in environments with imperfect or incomplete data - able to deliver value without requiring a perfect starting point. 

1. Bachelor's degree in Computer Science, Information Science, Engineering, or equivalent professional experience. 

Preferred Qualifications:

1. Experience in the upstream oil and gas industry or other asset-intensive industries, including familiarity with operational workflows such as production reporting, land management, or field data capture. 

1. Familiarity with cloud-based AI services on AWS and/or Azure, including managed ML platforms and LLM APIs. 

1. Experience deploying AI at a mid-size or growth-stage company where you had to build capability from scratch. 

1. Background in change management or organizational transformation in addition to technical delivery. 

1. Graduate degree (M.S. or Ph.D.) in a quantitative or computational field.