Description

 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.

Key Skills

Education

Bachelor's degree