Principal AI Engineer

Our client is a global leader in smart building technologies, energy solutions, and industrial automation. The company provides products and services that optimize energy efficiency, sustainability, and performance across buildings, industrial facilities, and vehicles.

VARITE is an award-winning minority business enterprise providing global consulting and staffing services to Fortune 1000 companies and government agencies. With 850+ global consultants, VARITE delivers excellence through scientific, engineering, technical, and non-technical staff augmentation and talent acquisition services.

Duration: 3-4 months | Location: Glendale, WI | Work Mode: Hybrid | Pay Rate Range: $100.00 - $110.00/hr on W2

About the role

Our client is integrating AI into the operation of high-demand buildings such as datacenters, hospitals, pharmaceutical facilities, and commercial campuses. We are transforming 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 foundational architecture assets and deployment playbooks that will accelerate AI adoption across our smart building platform. While scoped as a 3-4 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.

Responsibilities

• Collaborate daily with Controls SW engineers, architects, and product managers.
• Partner with internal domain experts who will onboard you to the platform—deep client knowledge is not expected on Day 1.
• Operate with principal-level autonomy: 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.
• Produce clear, durable architecture artifacts—reference architectures, decision records, playbooks—that engineers can execute without you in the room.
• Run technical workshops or design sessions when needed.

Requirements

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 that engineers can execute without you in the room.
• Comfortable working collaboratively in an embedded team model; can give and receive direct technical feedback.

Qualifications

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.

Benefits

• Health Insurance: Medical, dental, and vision coverage.
• Retirement Plans: Participation in a company-sponsored retirement savings plan.
• Legal Service Plans: Offering access to attorneys for legal advice and representation.