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Rupak Chand
Junior ML Data Associate specializing in AI training data and LLM prompt evaluation
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About
Applied ML/embodied AI practitioner who built an on-device gesture-control system for smart-home lights using Raspberry Pi + camera, focusing on privacy-preserving real-time inference and hardware-constrained optimization (async pipeline + TF Lite INT8). Also made a high-impact architecture decision for an ML content evaluation/QA pipeline processing millions of annotated text samples weekly, reducing batch runtime from ~6 hours to ~40 minutes while lowering compute cost.
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Key Strengths
- Optimized on-device CV pipeline on Raspberry Pi (reduced dropped frames/latency by splitting capture/inference/MQTT into async threads with ring buffers)
- Model deployment optimization via TF Lite INT8 quantization for constrained hardware
- Strong grasp of embodied AI constraints (low-latency multimodal reasoning, streaming/event-driven architectures)
- Production-minded ML approach (modularization, automated train-to-deploy, determinism, observability, safety hooks, continuous feedback loops)
- High-impact technical decision-making under ambiguity (chose alternative to cloud Spark; cut batch runtime from ~6 hours to ~40 minutes and reduced costs)
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