Lead AI Platform

Integrant is looking for game changers to join our team as " Lead AI Platform". The Lead AI Platform Engineer is responsible for bridging AI workloads with production-grade infrastructure, with a strong focus on NVIDIA AI stack, enabling high-performance, scalable, and optimized AI systems. This role focuses on model optimization, runtime efficiency, and GPU utilization, ensuring that AI workloads are production-ready, cost-efficient, and performant across enterprise environments. Roles and Responsibilities: Translate AI/ML workloads into optimized infrastructure and deployment strategies Optimize model performance across GPU environments (latency, throughput, memory utilization) Design and implement inference and training pipelines using NVIDIA stack tools (TensorRT, Triton, NIM) Convert and optimize models across frameworks (PyTorch → ONNX → TensorRT) Analyze and resolve performance bottlenecks using profiling tools (GPU, memory, network) Improve GPU utilization and scheduling efficiency across clusters Design scalable distributed training and inference architectures Work closely with customers to define AI infrastructure strategies and deployment models Support production deployments including monitoring, rollback, and performance validation Conduct applied research to improve model efficiency and infrastructure utilization Mentor team members on AI infrastructure, optimization, and GPU systems Experiment tracking tools (MLflow, W&B, Neptune) log parameters, metrics, and artifacts for comparison Find the Model degradation happens post-deployment: concept drift, data pipeline changes, traffic pattern shifts Root cause analysis (RCA) applies to ML systems: isolating variables, reproducing issues