Sr. Software Engineer

About the role

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.

What We Offer

• 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

About This Role

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. AI/ML Engineering: 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).

Required Qualifications

• 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
• GenAI & LLM Development: Hands-on experience building production applications across multiple LLM providers (e.g., Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, and open-source models)
• Experience with 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)
• Engineering Craft: 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

Preferred Qualifications

• 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