Role Summary

• Building production AI systems end to end, from prototype through deploy and monitor — owning the delivery dates and milestones you commit to, and flagging risks and contingencies early rather than reporting them after the fact
• Working directly with stakeholders across Engineering, Operations, HR, Finance, and Commercial to translate workflow problems into shippable tools, much of it in person and side by side with the teams whose work you are changing
• Writing the evaluation harnesses, prompts, retrieval logic, and integration code that make LLM-powered features reliable
• Establishing reusable technical patterns — prompt and model governance, eval frameworks, data access, deploy practices — built in partnership with IT so they fit our security, identity, and infrastructure standards
• Partnering with external vendors and implementation partners: evaluating their platforms, holding them to scope and schedule, and integrating what they deliver into our stack
• Contributing to the technical roadmap, including where to invest, what to kill, and what's worth building in-house versus buying
• Partnering with the Program Director on technical scoping for new use cases, including calling out when an out-of-the-box tool will solve the problem versus when custom work is justified
• Tracking how the AI tooling landscape is moving and folding what genuinely matters into our stack, including retiring approaches of your own when something better arrives

• A CS or related technical background, with hands-on production experience building AI/ML systems (internships, research, or full-time work all count)
• Demonstrated ability to ship: a portfolio of projects or work history that shows you take things from idea to running system, not just notebooks
• Solid software engineering fundamentals, including version control, testing, code review, and deployment practices
• Working knowledge of modern AI tooling: LLM APIs, RAG, evaluation, agent frameworks, vector stores
• Strong written and verbal communication. You'll be in front of non-technical stakeholders regularly and the work needs to translate
• A track record as a true business partner. You scope realistically, commit to dates and milestones, think through risks and contingencies in advance, and raise slippage early instead of at the deadline
• Ability to work as part of a team in a multi-stakeholder environment. There will be plenty of solo wins in this role, but many of the biggest ones will come through partnership with IT, the business, and outside vendors — and you should find that just as rewarding
• A habit of staying on top of technology developments. This tool set changes quarter to quarter; you will need to evaluate what is new, form a point of view, and adapt without chasing every headline
• Willingness to work in our hybrid, in-office culture — a minimum of three days per week in the Houston office, with a reasonable remote setup for the rest. We collaborate face to face with each other, with vendors, and with key stakeholders, and this role depends on that proximity
• Comfortable in ambiguity. We don't have a fully scoped roadmap; you'll help build it

• Production experience with hosted LLM platforms (Anthropic API, Azure OpenAI, AWS Bedrock, or equivalent)
• Hands-on experience with Claude Code, MCP server development, or agentic coding tools
• Familiarity with Microsoft ecosystem integration (Entra ID, Graph API, M365 surfaces) for enterprise AI deployments
• Experience with agent orchestration frameworks or multi-agent systems
• Exposure to settings where data sensitivity and operational safety constrain what you can deploy (OT, healthcare, finance, regulated industries)
• Background in energy, utilities, or other operationally complex sectors

• Having built something real people actually used, and having kept it running after launch. Prototypes and notebooks are not the same thing
• The ability to work inside enterprise constraints. To include identity and permissions, data you are not allowed to move, systems you do not control or clear evidence you would pick that up quickly
• Being in Houston and in the office at least three days a week
• Doing what you said you would do, by when you said you would do it