PakEnergy is accelerating its innovation. Help us lead it.

More than 2,300 oil and gas companies run their land, production, accounting and transportation on PakEnergy. We've started putting AI to work across that platform. Now we're accelerating our pace of innovation, and we're building the PakEnergy Innovation Lab to do it.

The Lab's mandate is simple: find the highest-value problems our customers face, prove solutions quickly with working prototypes built on real operational energy data, and take what works into production. We're hiring a Lead Applied AI Engineer to be the founding owner of generative AI and agentic systems on the Lab's cross-functional team, reporting directly to our Chief Product Officer. This is a hands-on lead role: you'll design and build the systems yourself while setting direction for the discipline. You'll have real autonomy. You own the generative AI architecture and technical choices on Lab projects, including build versus buy, help shape the opportunity backlog with the Chief Product Officer and the industry experts, and make the call, backed by evaluation results, when an idea should stop or GenAI isn't the answer. We value candor over polish: a fast, well-reasoned "this won't work" is a good outcome.

What We've Already Shipped With Pak AI

AI-powered lease provision analysis (PakEnergy Land). Embedded AI that flags key lease provisions in seconds instead of hours, so land teams can manage large portfolios and acquisitions faster.
PakCAPTURE™ (PakEnergy Transportation). Drivers photograph tickets, receipts and paperwork in the mobile app. Pak AI extracts the key data, auto-populates pickup and drop-off forms, and files the documents with the right load.
OnPing Plunger™ (OnPing). Optimization for plunger-lift wells. Pak AI learns each well's best historical runs, compares actual against expected performance, and tunes setpoints within the operator's automation and safety limits, or flags the well for human review. It has delivered up to 30% more production on conservatively set wells and 3.5–7% on wells that were already well optimized.

These are the first steps. The Innovation Lab is where the next ones get found, proven and shipped.

The team

The Innovation Lab brings together everyone it takes to go from a customer problem to a working product:
Industry experts for every PakEnergy product: Land, Production, Accounting and Transportation. They know the workflows, the documents, the data and the customers firsthand.
A Lead Data Scientist, your peer, who owns data science and classical machine learning: problem framing, modeling, experimentation and evaluation.
Solution engineers who take prototypes to customers and into production.
You, the Lab's founding owner of applied AI, designing and building its generative AI and agentic systems.
For a strong AI engineer, this means you won't be the lone "AI person" bolted onto a product team. Domain experts frame problems with you, your peer lead in data science brings the data and the evaluation rigor, and solution engineers put what you build in customers' hands.

Why this role

Founding owner of your discipline. You'll set the technical direction, architecture, standards and practices for applied AI in the Lab from day one.
Real documents, real operations. You'll work with lease and contract documents, field tickets and receipts, accounting records, and SCADA and IIoT data from OnPing, across a customer base of more than 2,300 companies. These are problems where accuracy matters and the results show up in customers' daily work.
Deep expertise around you. You'll work next to industry experts for every product, a peer lead in data science and solution engineers, so you can focus on building great AI systems.
Ownership from prototype to production. You design and build GenAI features yourself and get them into production with Engineering.
A direct line to leadership. You report to the Chief Product Officer, and your work shapes the Pak AI roadmap.
A path to leading a team. There are no direct reports today. As the Lab expands, this role is set up to hire and lead the Lab's applied AI team.

What You'll Do

Frame problems with the industry experts. Find where generative AI and agents can remove real work from customer workflows in Land, Production, Accounting and Transportation, and be clear about when GenAI isn't the right tool.
Architect LLM applications. Design and build retrieval-augmented generation, agents and tool use on top of PakEnergy's products and data.
Choose models and vendors wisely. Evaluate and select foundation models and providers, engineer prompts and context, and fine-tune where the evidence says it's worth it.
Build document AI. Build extraction and understanding for leases, contracts, tickets and other energy documents, with human review where accuracy demands it.
Make it safe, fast and affordable. Own guardrails and safety, protect customer data, and manage cost and latency with monitoring and fallbacks in place. Evaluate and mitigate security risks associated with agent actions, tool use, permissions and access to customer data.
Ship with Engineering. Get GenAI features into production inside PakEnergy products, with agent observability, tracing and operational monitoring to know how they're performing.
Partner with the Lead Data Scientist on data, evaluation and classical ML. They own the data work, lead evaluation design and build classical ML models. You build their evaluations into your systems, run them in production, and combine LLMs with their models where that gives the best result.
Pilot with solution engineers. Partner on customer pilots, configure and tune GenAI features for real customer data, and feed what you learn back into the build.
Set the technical direction for applied AI. Define the Lab's GenAI architecture, patterns, reusable components and standards for responsible generative AI.
Build the foundation for a future team. Document how applied AI work is done in the Lab, mentor and coach teammates, and shape the roles and hiring plan for when the Lab expands.

What You Bring

8+ years building production software or ML systems, including at least 2 years building LLM applications that you shipped to real users.
A track record of setting technical direction for AI or ML systems: choosing the architectures, tooling and standards that others adopted.
Experience mentoring, coaching or leading other engineers, formally or as a technical lead.
Deep hands-on experience with retrieval-augmented generation and agentic or tool-using systems in production, including prompt and configuration versioning, regression testing for model or prompt changes, rollback procedures for agent behavior changes, and a clear view of how these systems fail.
Sound judgment on model and vendor selection, prompt and context engineering, and when fine-tuning is, and isn't, worth it.
Experience making GenAI systems reliable in production: guardrails, evaluation in the loop, agent observability and tracing, and cost and latency management.
Strong, hands-on software engineering in Python (production services, APIs, testing) and experience deploying on a major cloud platform.
A track record of turning ambiguous problems into shipped products with domain experts, product and engineering, influencing executives and stakeholders along the way, and the candor to say when something won't work.

Nice to have

Oil and gas, energy or industrial domain experience is a plus, not a requirement. The Lab's industry experts bring deep knowledge of every PakEnergy product, and you'll learn the domain working beside them.
Document AI experience: OCR, layout-aware or multimodal extraction from contracts, forms or scanned documents.
Fine-tuning or deploying open-weight models.
Azure and/or AWS AI services (for example Azure OpenAI or Amazon Bedrock).
Experience hiring AI or ML engineers or building an applied AI function from an early stage.
Experience on cross-functional or innovation teams that take prototypes to customers.

Education: A degree in computer science or a related field, or equivalent practical experience. An advanced degree is welcome but not required.

Location: In office — Frisco, Texas or Calgary, Alberta (preferred). Exceptional candidates may be considered for a remote arrangement with a required onsite cadence.

Benefits: A casual business work environment, generous PTO, employer-sponsored health benefits, and a retirement savings plan (401(k) in the US).

Equal opportunity

PakEnergy is an equal opportunity employer. We welcome applicants of all backgrounds and are committed to an inclusive hiring process.

If you're excited about this role but don't check every box, we'd still like to hear from you.

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