Lead AI Platform

Description

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
• Use experiment tracking tools (MLflow, W&B, Neptune) to log parameters, metrics, and artifacts for comparison
• Identify model degradation post-deployment such as concept drift, data pipeline changes, and traffic pattern shifts
• Perform root cause analysis (RCA) for ML systems by isolating variables and reproducing issues

Requirements

• 8+ years of experience in AI systems
• 8+ years of experience in ML systems, HPC, and AI infrastructure
• Strong proficiency in Python
• Strong experience with GPU-based AI workloads and performance optimization
• Deep understanding of model optimization techniques (quantization, pruning, batching)
• Hands-on experience with:
  • PyTorch
  • ONNX / ONNX Runtime
  • TensorRT / TensorRT-LLM
  • Triton Inference Server
• Knowledge of CUDA, cuDNN, and GPU architecture fundamentals
• Experience with distributed systems (multi-GPU / multi-node)
• Familiarity with:
  • NCCL communication
  • NVLink / InfiniBand
  • Kubernetes or Slurm for orchestration
• Experience deploying AI models into production environments
• Ability to analyze system bottlenecks (compute, memory, network)
• Experience with profiling tools (Nsight, TensorRT profiler, etc.)
• Knowledge of cost optimization strategies for GPU workloads
• Use experiment tracking tools (MLflow, W&B, Neptune) to log parameters, metrics, and artifacts for comparison
• Identify model degradation post-deployment such as concept drift, data pipeline changes, and traffic pattern shifts
• Perform root cause analysis (RCA) for ML systems by isolating variables and reproducing issues

Nice to have

• Experience with NVIDIA NIM and NGC ecosystem
• Exposure to Megatron-LM, NeMo, or large-scale LLM training/inference
• Experience with LLM optimization techniques (KV cache, batching strategies)
• Familiarity with MLOps practices and CI/CD for AI systems
• Experience in customer-facing architecture or consulting roles
• Familiarity with hybrid cloud / on-prem HPC environments

Benefits

• Salary paid in USD
• Six-month career advancing opportunities
• Supportive and friendly work environment
• Premium medical insurance for employee and family
• English language development courses
• Interest-free loans paid over 2.5 years
• Technical development courses
• Planned overtime program (POP)
• Employment referral program
• Premium location
• Social insurance