Principal Architect, AI Enablement (Houston, TX Preferred OR United States - Remote)

Principal Architect, AI Enablement

Location: Houston, Texas (preferred) or United States - Remote

Model of Work: Hybrid if located in Houston, TX or Remote with Travel if the work location is United States – Remote

Are you excited by challenges? Do you enjoy working in a fast-paced, international and dynamic environment? Then now is the time to join Quorum Software, a rapidly growing company and industry leader in oil & gas transformation.

Quorum Software is the world’s largest provider of digital technology focused solely on business workflows that empower the next evolution of energy. From emerging companies to supermajors, throughout every region of the globe, customers rely on Quorum’s proven innovation and unmatched global expertise to streamline business operations and make data-driven decisions that optimize profitability and growth. Our industry-leading solutions are transforming energy companies across the entire value chain, helping visionary leaders evolve their organizations into modern energy companies.

Overview

We’re looking for a Principal, AI Enablement to help engineering teams put Quorum’s AI Development Lifecycle (AIDLC) into practice. You will work directly with teams to improve how they use AI across planning, design, implementation, testing, and review, and help stakeholders understand the progress, results, and priorities of that work.

Reporting to the Director of AI Enablement, you will work closely with them to turn AIDLC priorities into practical improvements across engineering teams. You will own the engineering standards and reference implementations that support adoption, build reusable tools and workflows, coach engineers, and measure results to guide the program’s next steps.

Communication is central to this role. You will tie technical activity and team experience to outcomes, explain challenges, and plan next steps. You will prepare and deliver stakeholder updates in partnership with the Director, track adoption and performance, and recommend changes based on results and team feedback.

This is a hands-on individual contributor role that leads through technical judgment, teaching, and influence across teams. You will use your production engineering experience to build tools and working examples, pair with engineers, conduct training, and help teams apply new practices to their own codebases.

Responsibilities

Architecture and Standards

• Translate the agreed AIDLC into practical engineering standards and reference implementations covering planning, specification, design, implementation, testing, review, and release.

• Define essential repository artifacts, including agent instruction files, context and specification documents, and review templates, with conventions that accommodate different languages and technology stacks. Define quality gates for AI-generated code with engineering and security leaders, including required automated

  validation and points where human review and approval are needed.

Platform and Tooling

• Build reusable starter kits, repository templates, shared agent skills, and approved MCP integrations so teams can adopt the standards with minimal effort.
• Establish repeatable ways to provide agents with relevant context and instructions across different codebases and languages.

• Integrate agreed AIDLC checks into CI/CD so automated validation runs as part of delivery.

  Evaluate AI development tools, models, and integrations, explain their tradeoffs, and recommend what to standardize on.

Governance and risk

• Work with Security, Legal, and engineering leaders to define and implement guardrails for code and data sent to models, secrets handling, prompt injection, and licensing risks in generated code.
• Implement appropriate logging and provenance for agent actions and AI-generated changes to support review, troubleshooting, and audit.

Adoption and enablement

• Partner with the Director of AI Enablement to set adoption priorities, identify teams needing support, and coordinate improvements with technical leaders across teams.

• Work with engineering teams to understand their workflows, identify adoption barriers, and plan their path onto the

  agreed standards.

• Pair with engineers on real delivery work to establish practical AI-assisted workflows in their own codebases.
• Develop and conduct training, workshops, and demonstrations on using AI throughout software de…