Data Jobs in Glendale, WI

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30d ago

AI Engineer

Johnson Controls Glendale, WI

Glendale, WI

30d ago Closed

$85k - $127k/yr Full time Agentic Ai Ai Agent Ai Engineer

This position is no longer accepting applications (closed Sep 22, 2026).

Job Description

This AI/ML and GenAI Engineer role is embedded in a scrum team to deliver production AI/ML and GenAI capabilities that enhance smart building products. The position focuses on hands-on model development, MLOps, LLM feature implementation (including RAG and agentic workflows), and developer tooling to increase delivery velocity for the Controls Software organization.

Key Responsibilities

Design, build, and deploy AI/ML models and GenAI capabilities into smart building products across cloud, edge, and on-prem environments
Develop LLM-powered features for operator copilots, intelligent alarm management, and natural language interfaces for building operations
Build and maintain data pipelines, model integration layers, and inference infrastructure to support real-time BAS use cases
Implement RAG architectures, agentic workflows, and prompt engineering patterns for production GenAI applications
Contribute to MLOps practices including model versioning, monitoring, evaluation, and continuous improvement pipelines
Identify and implement AI-assisted developer tooling to accelerate product development, including code generation, test automation, CI/CD intelligence, and review workflows
Mentor engineers on AI/ML and GenAI engineering practices to raise team capability over time
Define and document reusable AI engineering patterns, reference implementations, and best practices
Partner with data scientists and architects to translate research and prototypes into production-ready systems
Support roadmap and scoping conversations using AI feasibility and complexity assessments grounded in hands-on experience
Work in an agile scrum team in Milwaukee with engineers, data scientists, and product managers

Required Qualifications

7+ years of software engineering experience, with at least 5 years building and deploying AI/ML systems in production
Hands-on experience across the full ML lifecycle: data preparation, model training, evaluation, deployment, monitoring, and retraining
Strong machine learning fundamentals, including 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 such as experiment tracking, model registries, deployment pipelines, and observability
Hands-on experience building production applications across multiple LLM providers, including 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 and multi-agent orchestration, including tool-calling patterns; familiarity with Model Context Protocol (MCP) and tools such as LangGraph, CrewAI, LlamaIndex, or custom implementations
Strong evaluation discipline, including designing and operating LLM evaluation pipelines using eval datasets, LLM-as-judge techniques, and regression testing for prompts and model behavior
Experience with LLM observability and tracing, including instrumenting model calls, tool calls, and retrievals in production (e.g., LangSmith, LangFuse, or OpenTelemetry GenAI conventions)
Strong software engineering fundamentals including clean code, system design, API development, and distributed systems
Experience with cloud platforms (Azure preferred) and containerized deployment using Docker and Kubernetes
Comfort working in an agile scrum team, shipping iteratively, participating in design reviews, and writing maintainable code
Ability to clearly communicate technical concepts to non-technical stakeholders and influence product decisions using data
Professional fluency in English (written and spoken)
U.S. Citizenship and/or permanent residency is required; sponsorship is not available for this role

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 such as model selection, caching, token efficiency, and routing strategies

Technologies

Python, PyTorch, TensorFlow, scikit-learn
LLM providers: Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, and open-source models
RAG architectures, vector databases, embedding pipelines
Agentic frameworks, Model Context Protocol (MCP), LangGraph, CrewAI, LlamaIndex
MLOps tooling including experiment tracking, model registries, and observability
LLM evaluation and observability practices including LLM-as-judge techniques, LangSmith, LangFuse, and OpenTelemetry GenAI conventions
Azure, Docker, Kubernetes, OpenTelemetry
CI/CD intelligence

Benefits

Competitive salary
Paid vacation/holidays/sick time
Comprehensive benefits package including 401K, medical, dental, and vision care
On-the-job and cross-training opportunities
Encouraging and collaborative team environment
Dedication to safety through the Zero Harm policy

Compensation

$85,000 - $127,000 per year

Salary to be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, and alignment with market data
The posted salary range reflects the target compensation for this role
Exceptional candidates may bring unique skills and experiences that exceed the typical profile
The position includes a competitive benefits package

Location and Work Model

Glendale, WI (hybrid)
Scrum team embedded in Milwaukee

What Success Looks Like

AI/ML and GenAI features ship in smart building products and deliver measurable customer value
Developer tooling and AI-assisted workflows reduce cycle time for the Controls Software team
Engineers mentored on the team independently apply AI/ML and GenAI patterns to new problems
Trusted technical influence on how AI is designed, prioritized, and built across the roadmap
Team capacity to run AI-powered programs increases through your presence

Additional Notes

For an efficient and fair hiring process, technology-assisted tools, including artificial intelligence, may be used to help identify and evaluate candidates
All hiring decisions are ultimately made by human reviewers

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