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

- Strong Python and PyTorch skills.

- Experience deploying ML models in production.

- Understanding of GPU inference optimization and CUDA fundamentals.

- Familiarity with Docker and Linux.

- Strong 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