Mission

Design and own the on-device AI engine that runs perception and alignment models on AR headsets and ruggedised edge hardware – in environments with unreliable or zero connectivity. Factories have bad WiFi. Construction sites have none. If your system fails offline, the product is dead.

System Ownership
- Primary: On-device inference runtime (model loading, execution, resource management)
- Primary: Model optimisation pipeline (quantization, pruning, graph optimisation → deployment-ready artefacts)
- Primary: Offline-first architecture (local queue, retry sync, conflict resolution)
- Primary: Edge data compression (point cloud + telemetry compression before cloud sync)
- Secondary interface: CV team (you deploy and optimise their perception models on-device)
- Secondary interface: Cloud/Backend team (you sync data to their ingestion APIs when connectivity resumes)
- Does NOT own: Model training/architecture design (CV/AI teams), MR rendering (MR team), cloud infrastructure (Backend team)

What You Will Build




- On-device inference engine – Serve TensorRT/CoreML/ONNX models on heterogeneous edge hardware (ARM SoCs, Apple Neural Engine, Qualcomm Hexagon DSP). Manage model lifecycle: load, warm-up, execute, swap.
- Model quantization & optimisation pipeline – Take FP32 trained models → INT8/FP16 quantized, graph-optimised, target-specific artefacts. Maintain accuracy within 1% of FP32 baseline while hitting latency targets.
- Offline-first data architecture – Local SQLite/LevelDB store for scan data, inspection results, telemetry. Queue-based sync with exponential backoff. Conflict resolution when offline edits collide with cloud state.
Edge data compression – Compress point clouds (Draco, custom octree encoding) and telemetry streams before upload. Target: 10× compression ratio with- Latency reduction architecture – Profile end-to-end inference pipeline (pre-processing → inference → post-processing). Identify and eliminate bottlenecks. Hit the 200ms total budg

📌 Edge AI (Hyderabad)
🏢 Quantumcona
📍 Hyderabad