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