Senior AI Engineer - AAET

SM-Energy Company•Denver, CO

6h•$180,000 - $220,000

About The Position

SM Energy is seeking a Senior AI Engineer to take promising AI capabilities from prototype to production. This individual contributor role sits in a small, two-person R&D pod within our Advanced Analytics and Emerging Technologies team, reporting to the team's IT Manager. Where the pod's R&D function scouts and proves what's possible, this role makes it real — hardening validated prototypes into first production deployments, building the platform foundations that make each deployment faster than the last, and working directly with data engineering and delivery teams to stand AI systems up against governed enterprise data. This is a role at the leading edge of scaling AI for an enterprise. The systems this role builds are early — often the first of their kind at SM Energy — and the engineering judgment required goes well beyond following established patterns: designing agentic architectures where few reference implementations exist, building evaluation and observability into systems whose behavior is probabilistic, and making sound decisions about reliability, security, and cost in territory the industry is still mapping. Ownership of any individual solution ends at first production deployment — steady-state operation transitions to delivery teams — but the platform capabilities this role incubates (agent infrastructure, evaluation harnesses, deployment patterns, sandboxes) compound over time and become foundations the broader organization builds on. This role requires software engineering competencies and experience. The right candidate has shipped and supported real production systems, has spent the last couple of years building LLM-based or agentic applications rather than just using AI tools, and is energized by turning ambiguous, fast-moving technology into infrastructure an enterprise can rely on.

Requirements

Production Engineering Discipline — Builds systems meant to be relied on. Instinctively considers failure modes, monitoring, security, and maintainability, and knows the difference between a demo that works and a system that keeps working.
AI Systems Judgment — Understands the specific engineering challenges of LLM-based and agentic systems: non-deterministic behavior, evaluation difficulty, prompt and context management, cost dynamics, and failure modes that traditional software doesn't have. Designs accordingly.
Pragmatic Architecture — Makes sound build-vs-adopt decisions in a fast-moving ecosystem. Avoids both over-engineering for scale that may never come and under-engineering systems the business will depend on.
Cross-Team Collaboration — Works effectively across data engineering, delivery, security, and infrastructure teams. Builds systems others can operate, and treats a clean handoff as part of the job rather than an afterthought.
Managing Ambiguity — Comfortable being the first to solve a problem at SM Energy, without established internal patterns to follow. Finds or creates the reference implementation rather than waiting for one.
Communication — Explains architectural decisions and tradeoffs clearly to technical and non-technical audiences, and documents work so it outlives their direct involvement.
Strong software engineering fundamentals: Python and/or TypeScript, API design and integration patterns, version control, testing, and CI/CD
Hands-on production experience with the modern AI stack: LLM APIs and SDKs, agentic frameworks and orchestration, retrieval-augmented generation, Model Context Protocol (MCP) or similar tool-use protocols, and evaluation and observability approaches for AI systems
Cloud platform experience, Azure strongly preferred: container platforms, identity and access management, networking, and cost management
Experience working with enterprise data platforms (e.g., Snowflake) and production data pipelines
Familiarity with enterprise AI platforms and developer tooling (e.g., Claude, Azure AI services, agent development kits) preferred

Nice To Haves

Energy industry experience preferred but not required

Responsibilities

Carry validated AI prototypes from proof of concept through first production deployment — re-architecting for reliability, security, observability, and cost as needed
Partner closely with data engineering to connect AI systems to governed enterprise data platforms and production pipelines
Design, build, and incubate shared AI platform capabilities — agent runtime and orchestration infrastructure, evaluation and testing harnesses, deployment patterns, and sandbox environments — that make each successive deployment faster and safer
Transition steady-state ownership of deployed solutions to delivery and support teams with clean documentation, runbooks, and defined transition support
Establish and document engineering standards for AI systems — evaluation practices, monitoring approaches, security patterns, and cost management — that delivery teams can adopt
Work with the team during prototyping to keep proofs of concept production-viable — flagging architectural dead ends early rather than after handoff
Collaborate with platform, security, and infrastructure teams to ensure AI systems meet enterprise requirements for identity, access, data governance, and operational support
Evaluate the production-readiness of emerging AI infrastructure and tooling, and deliver honest assessments of what is and isn't ready for enterprise use
Document architectures, decisions, and reusable patterns so knowledge compounds across the pod and the broader team
Other duties as assigned