AI Engineer

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