Note: The job is a remote job and is open to candidates in USA. Johnson Controls is a global leader in smart, healthy, and sustainable buildings. The AI Engineer will build and deploy production AI/ML and GenAI capabilities for smart building products while strengthening AI engineering practices across the Controls Software team. The role partners with engineers, data scientists, and product managers to develop AI-enabled building operations solutions and accelerate software delivery.
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
Skills
- We require candidates to be U.S. Citizens and/or permanent residents of the United States
- Professional fluency in English (written and spoken) is required, as this role involves regular communication with customers, business partners, and colleagues in English
- This is a hybrid/remote role that is flexible on location so long as there is flexibility to travel to WI on a somewhat frequent basis
- 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
- Experience with 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
Benefits
- 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
- This is a hybrid/remote role that is flexible on location so long as there is flexibility to travel to WI on a somewhat frequent basis.
- Learning opportunities
- Career growth within a global engineering organization
Company Overview