Sr. AI/ML Engineer

Johnson Controls•Glendale, AZ

3d•$100,000 - $140,000•Remote

About The Position

As a global leader in smart, healthy and sustainable buildings, our mission is to reimagine the performance of buildings to serve people, places and the planet. Join a winning team that enables you to build your best future! Our teams are uniquely positioned to support a multitude of industries across the globe. You will have the opportunity to develop yourself through meaningful work projects and learning opportunities. We strive to provide an experience focused on supporting physical, financial, and emotional wellbeing. Become a member of the Johnson Controls family and thrive in an empowering company culture where your voice and ideas will be heard.

Requirements

Experience delivering production AI/ML solutions with strong operational ownership.
Strong backend/API development (Python and/or C#/.NET).
Experience with LLMs and agent-based systems, including: RAG, tool/function calling, orchestration, workflow automation.
Experience with Azure AI ecosystem (Azure OpenAI, AI Foundry, AI Search, Document Intelligence).
Experience with Azure Integration Services (App Services, Functions, Container Apps).
Strong DevOps experience (Azure DevOps, CI/CD, release management).
Experience with containerization (Docker, Kubernetes) and microservices architecture.
Strong enterprise integration experience (APIs, databases, authentication systems).
Excellent communication skills and ability to partner with business stakeholders.
Bachelor’s degree in Computer Science, Engineering, or related field.
Must be based in or able to support U.S. time zones.

Responsibilities

Design, build, and deliver production-grade AI solutions that drive enterprise innovation.
Work directly with business teams to translate workflows into scalable AI applications.
Own solutions from development through deployment and ongoing support.
Translate business workflows into scalable AI/ML solutions.
Design and build LLM and agent-based applications (RAG, orchestration, automation).
Develop backend services and APIs for AI solutions.
Integrate solutions with enterprise systems (APIs, databases, authentication).
Own production lifecycle: deployment, monitoring, troubleshooting, and support.
Implement CI/CD pipelines and enforce best practices for security and reliability.
Build and deploy containerized, microservices-based applications.
Leverage Azure AI services to develop enterprise AI capabilities.