AI Engineer

• Proven, hands-on experience building AI and data applications from the ground up and taking them into production, not just prototyping.
• Demonstrated ability to evaluate and adopt new AI technologies and platforms as the field evolves, translating them into working systems.
• Comfort operating as a hands-on technical lead while also leading and developing a team, with experience managing groups ranging from a handful of people to a dozen or more.
• Working knowledge of the full lifecycle of deploying models and AI agents into production, including validation, orchestration, safety controls, and performance monitoring.
• Experience working with cloud data platforms such as Databricks and unifying data from multiple operational sources for use in AI systems.
• Bachelor’s degree in computer science, data science, engineering, or a related field, or equivalent practical experience.

Preferred Qualifications

• Experience delivering high-value AI use cases within the Oil & Gas industry.
• Experience building the business case for AI investments, including articulating expected value and return to non-technical executive stakeholders.
• Experience establishing responsible-AI practices, including documentation standards, human oversight, and monitoring and guardrails for models and agents running in production.
• Experience standing up or scaling a technical function from a small team into a larger one over time.
• Domain exposure to upstream oil and gas or another asset-intensive, industrial sector, or comparable comfort operating in a highly regulated, data-rich physical-operations environment.

Permian Resources (NYSE: PR) is currently seeking an AI Engineer in Midland, TX reporting to the Head of AI. This is a hands-on leadership role: from day one, the AI Engineer architects and delivers production AI systems directly, while also shaping how the company’s AI function grows and operates over time.

General Responsibilities

• Own hands-on architecture and delivery of production AI systems from day one, with full platform access across the company’s AI and data environment.
• Define and shape the AI function’s next roles, starting with a Knowledge Engineer who owns ontology control and a future ML Engineer who owns MLOps and monitoring for AI models and agents, rather than inheriting fixed job descriptions.
• Build and lead a small, high-leverage team over time, or operate with broad authority across a team built around the role: the growth path is intentionally flexible and shaped by the person in it.
• Extend influence beyond direct reports through a hub-and-spoke model, setting how AI systems integrate, operate, and improve across the broader organization.
• Own the full lifecycle of models and agents in production, from methodology selection through validation, orchestration, safety controls, and ongoing performance monitoring.
• Shape the company’s build-versus-buy strategy for AI: a mix today that shifts toward in-house build over time, with partners where it makes sense, and with anything currently sourced externally subject to future review for whether it should be built in-house instead.
• Contribute to a shared data and AI layer strategy: an integrated system that gives the company a single, connected way to act on its data, built on top of its existing Azure Databricks foundation.
• Retain decision authority over AI work executed by outside consultants or systems integrators, so the company directs the work rather than simply receiving it.
• Partner closely with the company’s data engineering team as it modernizes its data platform, moving from static tables toward real-time data products, providing requirements and design input without owning that team’s pipelines.