Sr. Software Engineer

Johnson Controls • Glendale, WI

52d • $85,000 - $127,000 • Onsite

About The Position

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. 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. 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.

Requirements

• 7+ years of software engineering experience, with 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 foundation in machine learning fundamentals — supervised/unsupervised learning, time-series modeling, anomaly detection, and predictive analytics
• Proficiency in Python and relevant ML frameworks (PyTorch, TensorFlow, scikit-learn, or equivalent)
• Experience with MLOps tooling: experiment tracking, model registries, deployment pipelines, and observability
• Hands-on experience building production applications across multiple LLM providers (e.g., 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)
• 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
• Experience with LLM observability and tracing — instrumenting model calls, tool calls, and retrievals in production (e.g., LangSmith, LangFuse, or OpenTelemetry GenAI conventions)
• Strong software engineering fundamentals: clean code, system design, API development, and distributed systems
• Experience with cloud platforms (Azure preferred) and containerized deployment (Docker, Kubernetes)
• Comfortable 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

Nice To Haves

• Experience in industrial, OT, IoT, or building automation environments
• Familiarity with time-series data platforms and protocols such as BACnet, MQTT, or OPC UA
• Experience with edge AI deployment and latency-constrained inference environments
• Background in energy systems, HVAC, fault detection & diagnostics, or predictive maintenance use cases
• Experience mentoring engineers or leading technical initiatives within a product team
• Familiarity with cybersecurity considerations in OT/IoT environments
• Experience implementing AI safety guardrails, content filtering, and governance controls for production GenAI systems
• Experience with LLM cost optimization — model selection, caching, token efficiency, and routing strategies

Responsibilities

• Design, build, and deploy AI/ML models and GenAI capabilities into our smart building products across cloud, edge, and on-prem environments
• Develop LLM-powered features including 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: model versioning, monitoring, evaluation, and continuous improvement pipelines
• Identify and implement AI-assisted developer tooling to accelerate product development — code generation, test automation, CI/CD intelligence, and review workflows
• Mentor engineers on the team in AI/ML and GenAI engineering practices, elevating team capability over time
• Define and document reusable AI engineering patterns, reference implementations, and best practices the team can build against
• Partner with data scientists and architects to translate research and prototypes into production-ready systems
• Contribute to roadmap and scoping conversations by bringing AI feasibility and complexity assessments grounded in hands-on experience

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 our Zero Harm policy

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed