Note: The job is a remote job and is open to candidates in USA. MARS Solutions Group is looking for an experienced Principal AI Engineer located in Wisconsin. The role involves embedding within the Controls Software team to produce foundational architecture assets and deployment playbooks that will accelerate AI adoption across smart building platforms.
Responsibilities
- 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
Skills
- 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
- 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)
- 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
- 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
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