> Markdown version of [/jobs/ext/2170073-ai-engineer-aaet](https://www.wearedevelopers.com/jobs/ext/2170073-ai-engineer-aaet). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# AI Engineer - AAET

- **Company:** SM Energy
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Reference Implementation, Microsoft Access, Artificial Intelligence, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, Data Governance, Programming Tools, Identity and Access Management, Python (Programming Language), Operational Databases, Azure Machine Learning, Software Engineering, TypeScript, AI Infrastructure, Enterprise Data Management, Retrieval-Augmented Generation, Large Language Models, Snowflake, Multi-Agent Systems, Containerization, AI Platforms, Information Technology, SAP Ariba, Api Design, Software Version Control
- **Published:** August 21, 2026
- **Apply:** https://www.dice.com/job-detail/213550ed-d6b2-4d2c-9eb8-ed0e9144b115

## About the Role

* 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, * Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent demonstrated technical experience, * 5+ years of professional software engineering experience building and shipping production systems
 * 2+ years of hands-on experience designing, building, and deploying LLM-based or agentic AI systems (e.g., RAG pipelines, agent frameworks, model integrations, evaluation harnesses)
 * Experience collaborating with data engineering teams on production data pipelines and cloud platforms (Azure preferred)
 * Energy industry experience preferred but not required

## Description

* 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

Key Competencies

 * 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


## Related Videos

- [Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure)
- [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team)
- [API Design - Getting Started](https://www.wearedevelopers.com/videos/33-api-design-getting-started)
- [Inside the AI Revolution: How Microsoft is Empowering the World to Achieve More](https://www.wearedevelopers.com/videos/869-inside-the-ai-revolution-how-microsoft-is-empowering-the-world-to-achieve-more)
- [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean)
- [Rest API Antipatterns](https://www.wearedevelopers.com/videos/100208-rest-api-antipatterns)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)