Product Lead — IoT & Digital Twin Platform

Job Summary

• We are building the next-generation smart facilities platform — one that goes far beyond traditional BMS. At its core is a live digital twin of every building we deploy in a real-time, physics-informed virtual replica of its mechanical, electrical, and environmental systems.
• This role is for a hands‑on Product Lead who can both architect and lead. You will own the full stack from edge device to cloud analytics, define product direction, and mentor a growing engineering team. If you have deep expertise at the intersection of IoT, controls, and scalable software — and are excited by the idea of buildings that think — this role is for you.

Responsibilities

• End-to-end platform architecture:
  • Edge → Gateway → Cloud / On‑Prem → Analytics → Control feedback loop
• Digital Twin Development:
  • Build and maintain real-time building digital twins using physics‑based and data‑driven models
  • Model HVAC topology, asset hierarchies, and operational states in a live twin environment
  • Integrate BIM data, sensor streams, and historical operations data into the twin
• Protocol & Systems Integration:
  • BACnet, Modbus, OPC‑UA, MQTT — connecting real building systems to the platform
  • Real‑time data ingestion pipelines and time‑series analytics engines
• Control strategy development: HVAC optimization, demand response, fault detection
• Product thinking: translate facility‑level problems into software abstractions and roadmap
• Mentor and lead a team of 5–6 engineers across backend, integration, and analytics

Education and Experience

• 5+ years in IoT / Industrial IoT / Building Automation platforms
• Deep understanding of HVAC systems: chillers, AHUs, VAVs, VRF, cooling towers
• Hands‑on experience with BACnet, Modbus, OPC‑UA, MQTT

Digital Twin experience

• Building or industrial digital twin platforms (e.g., Azure Digital Twins, AVEVA, Siemens Xcelerator, custom)
• Working knowledge of twin modelling concepts: ontologies, asset graphs, state synchronization
• Strong backend engineering in Python — the primary language for data pipelines, analytics, and AI integration

AI & Machine Learning Integration

• Experience embedding AI/ML models into production systems: predictive maintenance, anomaly detection, load forecasting
• Familiarity with LLM APIs (OpenAI, Anthropic) or AI orchestration frameworks (LangChain, LlamaIndex) for building intelligent features

Experience with time‑series databases

• InfluxDB, TimescaleDB, or similar
• Proven ability to lead engineering teams and drive product decisions