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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, 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 (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., LangGraph, CrewAI, LlamaIndex).
• 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 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.

AI/ML and GenAI features you build are shipping in our smart building products, delivering measurable value to customers. Developer tooling and AI‑assisted workflows you introduce meaningfully reduce cycle time for the Controls SW team. Engineers you mentor independently apply AI/ML and GenAI patterns to new problems. Your technical voice shapes how AI is prioritized and built across the roadmap, directly growing the team’s capacity to run AI‑powered programs.

Competitive salary ($85,000 – $127,000) with a comprehensive benefits package—including 401K, medical, dental, vision, paid vacation/holidays/sick time, and on‑the‑job cross‑training opportunities.

Johnson Controls International plc. is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, genetic information, sexual orientation, gender identity, status as a qualified individual with a disability, or any other protected characteristic.

To view more information about your equal‑opportunity and non‑discrimination rights as a candidate, visit EEO is the Law. If you are an individual with a disability and require an accommodation during the application process, please visit here.