Sr. Software Engineer

About this Role

Johnson Controls is bringing AI into the operation of the world’s most demanding buildings—from datacenters to hospitals and commercial campuses. The Senior AI/ML Engineer sits at the center of this transformation, building production AI/ML and GenAI capabilities for our smart building products while raising the AI engineering capability across the Controls Software team.

Your work is split into two equally weighted pillars:

• Pillar 1 — Build AI Products: 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 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: model versioning, monitoring, evaluation, and continuous improvement pipelines.
• Identify and implement AI‑assisted developer tooling—code generation, test automation, CI/CD intelligence, and review workflows—to accelerate product development.
• Mentor team engineers in AI/ML and GenAI engineering practices, elevating team capability over time.
• Define and document reusable AI engineering patterns, reference implementations, and best practices for the team to 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.

Pillar 2 — Accelerate the Team: 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.

• 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, Tensor Flow, 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 (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 (e.g., Lang Graph, CrewAI, Llama Index).
• 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.
• 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 maintainable code.
• Ability to communicate technical concepts clearly to non‑technical stakeholders and influence product decisions with data.

• Experience in industrial, OT, IoT, or building‑automation environments.
• Familiarity with time‑series data platforms and protocols such as BACnet, MQTT, or OPCUA.
• 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…