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