Live opening · Posted 6 days ago
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Location: Hyderabad & Bangalore
Experience: 8+ years
Must-Have Skills
8–10 years in software engineering, with 3+ years in on-device / edge AI deployment.
Strong C++ and Python.
Hands-on with embedded inference runtimes — LiteRT/TFLite, ONNX Runtime, ExecuTorch, TVM or vendor NPU SDKs — including delegate/execution-provider integration.
Practical quantization expertise — INT8/INT4, per-channel schemes, calibration, accuracy recovery.
Model formats and conversion tooling across PyTorch/TensorFlow to deployable graphs.
Profiling on heterogeneous SoCs and reasoning about memory bandwidth as the dominant constraint.
Understanding of CNN, transformer and modern vision/language model architectures.
Good-to-Have Skills
On-device LLM/VLM deployment — llama.cpp class runtimes, speculative decoding, paged KV cache.
Custom operator or kernel development for DSP/NPU/GPU (OpenCL, Vulkan compute, DSP intrinsics).
Compiler-level work — MLIR, TVM, graph-level optimization passes.
Vision pipeline integration and camera-to-inference zero-copy paths.
MLOps for edge — model versioning, A/B evaluation, field accuracy monitoring.
Work arrangement
No
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