Director Raisers Edge Database Jobs in Wisconsin

The Company is bringing AI into the way the world\'s most demanding buildings operate from datacenters and hospitals to pharmaceutical facilities and commercial campuses. We are transforming our smart building products into AI-native platforms capable of autonomous operations, intelligent operator assistance, and scalable AI deployment across cloud, edge, and on-premises environments.
This is a focused, time-boxed engagement for a Principal-level AI engineer to embed within the Controls Software team and produce the foundational architecture assets and deployment playbooks that will accelerate AI adoption across our smart building platform at scale. While scoped as a 34 month engagement, there is potential for extension based on scope evolution and mutual fit.
You will work hands-on alongside our engineers, learn our platform (with full internal expert support), and apply your deep AI systems expertise to create artifacts the team will execute against beyond the engagement.
How you will work:
Collaborate daily with Controls SW engineers, architects, and product managers
Partner with internal domain experts who will onboard you to the platform deep BAS knowledge is not expected on Day 1
Operate with principal-level autonomy: you drive the architecture, validate assumptions with the team, and produce deliverables iteratively
Participate in team rituals (standups, design reviews, retrospectives) as a full team member for the duration of the engagement
Required Qualifications:
AI Systems & Architecture
10+ years of hands-on software or systems engineering experience, with at least 6 years focused on AI/ML in production environments
Proven experience designing and deploying AI/ML systems at scale from data ingestion through inference and monitoring
Deep knowledge of MLOps: model deployment pipelines, versioning, observability, drift detection, and continuous improvement
Experience with edge-to-cloud AI execution strategies: balancing latency, cost, and resiliency across distributed environments, including LLM cost optimization (model selection, caching, routing)
Strong command of data pipeline architecture, time-series data, event-driven systems, and API/microservices patterns
GenAI & LLM
Hands-on experience architecting production GenAI applications across multiple LLM providers (e.g., Anthropic, OpenAI, AWS Bedrock, Azure OpenAI, and open-source models)
Deep knowledge of RAG architectures, vector databases, embedding pipelines, and retrieval strategies at production scale
Experience with agentic architectures, multi-agent orchestration, and tool-calling patterns including emerging standards like Model Context Protocol (MCP)
Experience with LLM observability and tracing instrumenting model calls, tool calls, and retrievals in production (e.g., LangSmith, LangFuse, or OpenTelemetry GenAI conventions)
Craft & Communication
Ability to produce clear, durable architecture artifacts reference architectures, decision records, playbooks that engineers can execute without you in the room
Comfortable working collaboratively in an embedded team model; can give and receive direct technical feedback
Capable of running technical workshops or design sessions when needed
Preferred Qualifications
Experience in industrial, OT, or IoT environments (building automation, manufacturing, energy, or similar)
Familiarity with protocols such as BACnet, MQTT, Modbus, or OPC UA
Exposure to cybersecurity frameworks in OT environments (e.g., IEC 62443, NIST CSF)
Experience with AI use cases in buildings or critical infrastructure: FDD, energy optimization, predictive maintenance, alarm intelligence
Experience with containerization, CI/CD tooling, and observability platforms
Experience operationalizing AI safety guardrails, content filtering, and governance controls in production GenAI systems
What Success Looks Like:
A validated AI reference architecture that the Controls SW team can build against confidently
Deployment playbooks that are immediately usable not aspirational documents
Engineers who have meaningfully leveled up through collaboration with you
Clear architectural foundation for our smart building platform to evolve toward autonomous, AI-native building operations
Enable Skills-Based Hiring: No
Dimensions Punch Clock Entry
No
Is driving required for the assignment
No
Is personal vehicle mileage reimbursable
(No Value)
Is this a government funded project
No
Will this position be remote
Hybrid - mix of worksite/office and remote