Full Description
Key Responsibilities:Build & Deploy AI Solutions: Implement end-to-end AI systems (LLMs, computer vision, voice transcription, multimodal AI) using Python, PyTorch/TensorFlow, and Docker/Kubernetes.Optimize AI Models: Fine-tune/train LLMs (e.g., GPT-4, Llama 2, RAG pipelines), vision models (CNNs, ViTs), and speech models (Whisper, VITS) for low-latency inference.AI Pipeline Engineering: Design scalable data preprocessing, training, and serving pipelines (e.g., Ray, Kubeflow, Airflow).
Edge/Cloud Deployment: Containerize models (Docker) and deploy on K8s, AWS SageMaker, or edge devices.
Performance Tuning: Benchmark and optimize models for GPU/TPU acceleration (CUDA, TensorRT).
Required Skills:5+ years in Python AI development (not just research—production experience required).
Hands-on with LLMs (LangChain, Hugging Face), computer vision (OpenCV, YOLO), and voice AI (ASR, TTS).
Strong MLOps skills: Docker, CI/CD for AI, model registries (MLflow, Weights & Biases).
Experience with distributed training (FSDP, DeepSpeed, Horovod).
Nice-to-Have:NVIDIA Triton Inference Server, ONNX Runtime.
Quantization/pruning for model optimization.
CUDA-level performance debugging.
Important: Should have transferable Iqama.Job Type: Full-timeApplication Question(s):Please confirm do you have transferable Iqama?
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