Lead Ai Platform

Job Summary

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
• 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

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:

1. PyTorch
2. ONNX / ONNX Runtime
3. TensorRT / TensorRT-LLM
4. Triton Inference Server

• Knowledge of CUDA cuDNN and GPU architecture fundamentals
• Experience with distributed systems (multi-GPU / multi-node)
• Familiarity with:

1. NCCL communication
2. NVLink / InfiniBand
3. 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
• 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

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 employee 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 in Maadi
• Social insurance

Apply Now