Sr. Software Engineer

Job Description

\n

Build your best future with the Johnson Controls team!

\n

Who we are:

\n

\n

Johnson Controls is 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 our employees with an experience focused on supporting their 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 – your next great opportunity is just a few clicks away!

\n

\n

What We Offer:

\n

\n
• \n

  Competitive salary

\n
• \n

  Paid vacation/holidays/sick time

\n
• \n

  Comprehensive benefits package including 401K, medical, dental, and vision care.

\n
• \n

  On-the-job/cross-training opportunities

\n
• \n

  Encouraging and collaborative team environment

\n
• \n

  Dedication to safety through our Zero Harm policy

\n

\n

 

\n \n

\n

About This Role  

\n \n

\n

Johnson Controls is bringing AI into the way the world's most demanding buildings operate — from datacenters and hospitals to pharmaceutical facilities and commercial campuses. We are transforming our smart building products into AI-native platforms that can reason about building operations, assist operators intelligently, and accelerate how our engineering teams build and ship software. 

\n \n

\n

 

\n \n

\n

This Senior AI/ML Engineer role sits at the center of that transformation. You will do two things in roughly equal measure: build production AI/ML and GenAI capabilities directly into our smart building products, and raise the AI engineering capability of the broader Controls Software team so we can run more programs, faster, with AI embedded in how we work. 

\n \n

\n

 

\n \n

\n

You will be embedded in a scrum team in Milwaukee, working hands-on with engineers, data scientists, and product managers. You will become a key technical voice on how AI is designed, built, and deployed across the Controls Software portfolio. 

\n \n

\n

 

\n \n

\n

What You’ll Do  

\n \n

\n

Your work falls into two equally weighted pillars: 

\n \n

\n

 

\n \n

\n

\n \n

\n

\n

Pillar 1 — Build AI Products  

\n \n \n

\n

\n

Pillar 2 — Accelerate the Team  

\n \n \n

\n

\n

AI/ML & GenAI Engineering  

\n \n

\n

\n
• \n

  Design, build, and deploy AI/ML models and GenAI capabilities into our smart building products across cloud, edge, and on-prem environments 

\n

\n \n

\n

\n
• \n

  Develop LLM-powered features including operator copilots, intelligent alarm management, and natural language interfaces for building operations 

\n

\n \n

\n

\n
• \n

  Build and maintain data pipelines, model integration layers, and inference infrastructure for real-time BAS use cases 

\n

\n \n

\n

\n
• \n

  Implement RAG architectures, agentic workflows, and prompt engineering patterns for production GenAI applications 

\n

\n \n

\n

\n
• \n

  Contribute to MLOps practices: model versioning, monitoring, evaluation, and continuous improvement pipelines 

\n

\n \n \n

\n

\n

Team Capability & Velocity  

\n \n

\n

\n
• \n

  Identify and implement AI-assisted developer tooling to accelerate product development — code generation, test automation, CI/CD intelligence, and review workflows 

\n

\n \n

\n

\n
• \n

  Mentor engineers on the team in AI/ML and GenAI engineering practices, elevating team capability over time 

\n

\n \n

\n

\n
• \n

  Define and document reusable AI engineering patterns, reference implementations, and best practices the team can build against 

\n

\n \n

\n

\n
• \n

  Partner with data scientists and architects to translate research and prototypes into production-ready systems 

\n

\n \n

\n

\n
• \n

  Contribute to roadmap and scoping conversations by bringing AI feasibility and complexity assessments grounded in hands-on experience 

\n

\n \n \n \n \n

\n

\n

 

\n \n

\n

 

\n \n

\n

Required Qualifications  

\n \n

\n

We are looking for a senior engineer who has shipped AI/ML and GenAI systems in production and can operate as a technical leader within a product scrum team. 

\n \n

\n

 

\n \n

\n

AI/ML Engineering  

\n \n

\n

\n
• \n

  7+ years of software engineering experience, with at least 5 years building and deploying AI/ML systems in production 

\n

\n \n

\n

\n
• \n

  Hands-on experience with the full ML lifecycle: data preparation, model training, evaluation, deployment, monitoring, and retraining 

\n

\n \n

\n

\n
• \n

  Strong foundation in machine learning fundamentals — supervised/unsupervised learning, time-series modeling, anomaly detection, and predictive analytics 

\n

\n \n

\n

\n
• \n

  Proficiency in Python and relevant ML frameworks (PyTorch, TensorFlow, scikit-learn, or equivalent) 

\n

\n \n

\n

\n
• \n

  Experience with MLOps tooling: experiment tracking, model registries, deployment pipelines, and observability 

\n

\n \n

\n

 

\n \n

\n

GenAI & LLM Development  

\n \n

\n

\n
• \n

  Hands-on experience building production applications across multiple LLM providers (e.g., Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, and open-source models) 

\n

\n \n

\n

\n
• \n

  Working knowledge of RAG architectures, vector databases, embedding pipelines, and retrieval strategies 

\n

\n \n

\n

\n
• \n

  Experience with agentic frameworks, multi-agent orchestration, and tool-calling patterns — including emerging standards like Model Context Protocol (MCP) (e.g., LangGraph, CrewAI, LlamaIndex, or custom implementations) 

\n

\n \n

\n

\n
• \n

  Strong evaluation discipline: ability to design, run, and reason about LLM evaluation pipelines — including eval datasets, LLM-as-judge techniques, and regression testing for prompts and model behavior 

\n

\n \n

\n

\n
• \n

  Experience with LLM observability and tracing — instrumenting model calls, tool calls, and retrievals in production (e.g., LangSmith, LangFuse, or OpenTelemetry GenAI conventions) 

\n

\n \n

\n

 

\n \n

\n

Engineering Craft  

\n \n \n

\n

\n

\n
• \n

  Strong software engineering fundamentals: clean code, system design, API development, and distributed systems 

\n

\n \n

\n

\n
• \n

  Experience with cloud platforms (Azure preferred) and containerized deployment (Docker, Kubernetes) 

\n

\n \n

\n

\n
• \n

  Comfortable working in an agile scrum team — shipping iteratively, participating in design reviews, and writing code others can maintain 

\n

\n \n

\n

\n
• \n

  Ability to communicate technical concepts clearly to non-technical stakeholders and influence product decisions with data 

\n

\n \n

\n

 

\n \n

\n

Preferred Qualifications  

\n \n

\n

\n
• \n

  Experience in industrial, OT, IoT, or building automation environments 

\n

\n \n

\n

\n
• \n

  Familiarity with time-series data platforms and protocols such as BACnet, MQTT, or OPC UA 

\n

\n \n

\n

\n
• \n

  Experience with edge AI deployment and latency-constrained inference environments 

\n

\n \n

\n

\n
• \n

  Background in energy systems, HVAC, fault detection & diagnostics, or predictive maintenance use cases 

\n

\n \n

\n

\n
• \n

  Experience mentoring engineers or leading technical initiatives within a product team 

\n

\n \n

\n

\n
• \n

  Familiarity with cybersecurity considerations in OT/IoT environments 

\n

\n \n

\n

\n
• \n

  Experience implementing AI safety guardrails, content filtering, and governance controls for production GenAI systems 

\n

\n \n

\n

\n
• \n

  Experience with LLM cost optimization — model selection, caching, token efficiency, and routing strategies 

\n

\n \n

\n

 

\n \n

\n

What Success Looks Like  

\n \n

\n

\n
• \n

  AI/ML and GenAI features you build are shipping in our smart building products and delivering measurable value to customers 

\n

\n \n \n

\n

\n

\n
• \n

  Developer tooling and AI-assisted workflows you introduce meaningfully reduce cycle time for the Controls SW team 

\n

\n \n

\n

\n
• \n

  Engineers you mentor are independently applying AI/ML and GenAI patterns to new problems 

\n

\n \n

\n

\n
• \n

  You are a trusted technical voice on the team — shaping how AI is designed, prioritized, and built across the roadmap 

\n

\n \n

\n

\n
• \n

  The team’s capacity to run AI-powered programs grows directly because of your presence 

\n

\n \n

\n

 

\n \n

\n

Why Johnson Controls  

\n \n

\n

\n
• \n

  Work on AI problems that have direct physical impact — buildings that use less energy, run more reliably, and operate more safely 

\n

\n \n

\n

\n
• \n

  Join a team that is actively investing in AI as a core product capability, not a side project 

\n

\n \n

\n

\n
• \n

  Collaborate with a multidisciplinary team of engineers, data scientists, product managers, and domain experts in building automation 

\n

\n \n

\n

\n
• \n

  Competitive compensation, benefits, and career growth within a global engineering organization 

\n

\n

\n

SALARY RANGE : $85,000 - $127,000 (Salary to be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data.)  This position includes a competitive benefits package. The posted salary range reflects the target compensation for this role. However, we recognize that exceptional candidates may bring unique skills and experiences that exceed the typical profile. If you believe your background warrants consideration beyond the stated range, we encourage you to apply. To support an efficient and fair hiring process, we may use technology assisted tools, including artificial intelligence (AI), to help identify and evaluate candidates. All hiring decisions are ultimately made by human reviewers. For details, please visit the About Us tab on the Johnson Controls Careers site at -us

\n

\n \n