Senior Technical Architect

About the role

Own the on-device AI execution stack for ARM-based SoCs with dedicated neural accelerators — model conversion and quantization, runtime and delegate integration, and deployment of vision, LLM and VLM workloads within edge power and memory budgets.

What you'll do

• Deploy and optimize neural network models on NPU, GPU and CPU backends using embedded inference runtimes (LiteRT/TensorFlow Lite, ONNX Runtime, ExecuTorch or vendor SDKs)
• Own the model conversion pipeline — graph capture, operator mapping, quantization (PTQ and QAT support), calibration and accuracy validation against reference
• Diagnose and resolve unsupported operators, graph partitioning and fallback behaviour; work with vendor toolchains on accelerator limitations
• Enable on-device LLM and VLM workloads — weight quantization, KV-cache management, prefill/decode optimization, memory footprint and token-throughput tuning
• Benchmark and optimize inference latency, throughput, memory bandwidth and energy per inference; publish reproducible performance data

What you need

• 8–10 yrs of experience
• LLMs
• Python
• MLOps
• C++
• TensorFlow
• PyTorch
• Any graduate