Responsibilities
Architect and scale a next-generation multimodal inference platform powering multiple production AI pipelines.
Building GPU-resident multimodal pipelines for image, audio, and video inference.
Optimizing PyTorch models with TensorRT and Torch-TensorRT.
Designing agile batching and low-latency inference systems.
Building GPU-native preprocessing pipelines using NVIDIA DALI and Kornia.
Supporting inference across heterogeneous GPU fleets (H100 A100 A10G, RTX 4090 etc. ).
Improving observability, throughput, reliability, and deployment automation.

Requirements
Solid Python and PyTorch skills.
Experience deploying ML models in production.
Understanding of GPU inference optimization and CUDA fundamentals.
Familiarity with Docker and Linux.
Robust debugging and problem-solving skills.
Experience with NVIDIA Triton, TensorRT, or DALI.
Familiarity with CUDA profiling and performance optimization.
Experience with distributed inference systems.
Knowledge of audio/video ML pipelines.
Experience with ONNX, AWS GPU infrastructure, and model quantization techniques (FP16/INT8).

This job was posted by Subrina Ahoy Lai from NeuralGarage.

📌 Senior Ml Engineer Bengaluru
🏢 NeuralGarage
📍 Bengaluru