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ClosedPosted 179 days ago

Lead AI Platform

Workable (global search)109,826 open roles

Where
Cairo, Cairo Governorate, Egypt

This posting closed on 2026-10-04. 109,826 other Workable (global search) postings are live.

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This job: posted 179 days ago

Workable (global search) median: 2 days open

The posting

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

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