Johnson Controls is building out production AI capabilities for smart building products, pairing hands-on model engineering with practical MLOps and developer tooling. This AI/ML & GenAI Engineer role sits inside a scrum team working with engineers, data scientists, and product managers to translate prototypes into systems that run across cloud, edge, and on-prem environments.
Based in Glendale, WI in a hybrid setup, the position focuses on shipping LLM features for building operations, implementing RAG and agentic workflows, and strengthening the Controls Software team’s ability to deliver AI-ready software.
Responsibilities
- Design, build, and deploy AI/ML models and GenAI capabilities into smart building products across cloud, edge, and on-prem environments
- Develop LLM-powered features such as operator copilots, intelligent alarm management, and natural language interfaces for building operations
- Build and maintain data pipelines, model integration layers, and inference infrastructure for real-time BAS use cases
- Implement RAG architectures, agentic workflows, and prompt engineering patterns for production GenAI applications
- Contribute to MLOps practices including model versioning, monitoring, evaluation, and continuous improvement pipelines
- Identify and implement AI-assisted developer tooling to accelerate delivery, including code generation, test automation, CI/CD intelligence, and review workflows
- Mentor team members on AI/ML and GenAI engineering practices to raise team capability over time
- Define and document reusable AI engineering patterns, reference implementations, and best practices
- Partner with data scientists and architects to move research and prototypes into production-ready systems
- Support roadmap and scoping conversations by providing AI feasibility and complexity assessments based on hands-on experience
- Be embedded in a scrum team in Milwaukee, working hands-on with engineers, data scientists, and product managers
Requirements
- 7+ years of software engineering experience, including at least 5 years building and deploying AI/ML systems in production
- Hands-on experience with the full ML lifecycle: data preparation, model training, evaluation, deployment, monitoring, and retraining
- Strong ML fundamentals, including supervised/unsupervised learning, time-series modeling, anomaly detection, and predictive analytics
- Proficiency in Python and relevant ML frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent
- MLOps tooling experience: experiment tracking, model registries, deployment pipelines, and observability
- Hands-on experience building production applications across multiple LLM providers, including Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, and open-source models
- Working knowledge of RAG architectures, vector databases, embedding pipelines, and retrieval strategies
- 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)
- Evaluation discipline: ability to design, run, and reason about LLM evaluation pipelines including eval datasets, LLM-as-judge methods, and regression testing for prompts and model behavior
- Experience with LLM observability and tracing including instrumenting model calls, tool calls, and retrievals in production (e.g., LangSmith, LangFuse, or OpenTelemetry GenAI conventions)
- Strong software engineering fundamentals such as clean code, system design, API development, and distributed systems
- Experience with cloud platforms (with Azure preferred) and containerized deployment using Docker and Kubernetes
- Comfort working in an agile scrum team: shipping iteratively, participating in design reviews, and writing code others can maintain
- Ability to communicate technical concepts clearly to non-technical stakeholders and influence product decisions with data
- Professional fluency in English (written and spoken) is required
- Must be U.S. Citizens and/or permanent residents of the United States; sponsorship is not available for this role
Technologies
- Python; PyTorch; TensorFlow; scikit-learn
- LLM providers: Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, open-source models
- RAG architectures; vector databases; embedding pipelines
- Agentic frameworks; Model Context Protocol (MCP); LangGraph; CrewAI; LlamaIndex
- MLOps tooling (experiment tracking), model registries, observability
- LLM evaluation using LLM-as-judge techniques; LangSmith; LangFuse; OpenTelemetry GenAI conventions
- Azure; Docker; Kubernetes; OpenTelemetry; CI/CD intelligence
Benefits
- Competitive salary
- Paid vacation/holidays/sick time
- Comprehensive benefits package including 401K, medical, dental, and vision care
- On-the-job/cross-training opportunities
- Encouraging and collaborative team environment
- Dedication to safety through the Zero Harm policy
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, and exceptional candidates may bring unique skills and experiences that exceed the typical profile.
Johnson Controls may use technology assisted tools, including artificial intelligence, to help identify and evaluate candidates, and all hiring decisions are made by human reviewers.
