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

• 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

• 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