Principal AI Architecture Engineer

About the role

Job Title: Principal AI Architecture Engineer

No - C2C

Only W2

Note

Must have - 10+ years of hands-on software or systems engineering experience, with at least 6 years focused on AI/ML in production environments

Description

The client 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 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.

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