We are developing a large-scale video monitoring and intelligent video analytics system for a government project.
• Develop production systems for video analytics based on Computer Vision
• Process dozens and hundreds of RTSP video streams with minimal latency
• Build and optimize pipelines using NVIDIA DeepStream, GStreamer, and TensorRT
• Develop and implement Computer Vision models (YOLO, ViT, Video Transformers, VLM)
• Optimize inference on GPU/CPU (TensorRT, OpenVINO, ONNX, Quantization)
• Integrate Computer Vision with LLM and multimodal models for generating intelligent analytics from video data
• Develop AI services using FastAPI/gRPC, Docker, Kubernetes
• Build RAG and Agentic AI solutions (LangGraph, vLLM, Function Calling)
• Design the architecture of AI components and participate in technical decision-making
• Process dozens and hundreds of RTSP video streams with minimal latency
• Build and optimize pipelines using NVIDIA DeepStream, GStreamer, and TensorRT
• Develop and implement Computer Vision models (YOLO, ViT, Video Transformers, VLM)
• Optimize inference on GPU/CPU (TensorRT, OpenVINO, ONNX, Quantization)
• Integrate Computer Vision with LLM and multimodal models for generating intelligent analytics from video data
• Develop AI services using FastAPI/gRPC, Docker, Kubernetes
• Build RAG and Agentic AI solutions (LangGraph, vLLM, Function Calling)
• Design the architecture of AI components and participate in technical decision-making
• Commercial experience in Computer Vision and Video Analytics
• Practical experience with RTSP, DeepStream, GStreamer, TensorRT
• Excellent knowledge of PyTorch, OpenCV, YOLO
• Experience in model optimization (ONNX, TensorRT, OpenVINO, CUDA)
• Understanding of modern Vision Transformers (ViT, DETR, Swin, TimeSformer, ViViT) and multimodal models (LLaVA, Qwen-VL)
• Experience with LLM, RAG, and agentic systems is a plus
• Experience in developing production AI systems and ML services
• Practical experience with RTSP, DeepStream, GStreamer, TensorRT
• Excellent knowledge of PyTorch, OpenCV, YOLO
• Experience in model optimization (ONNX, TensorRT, OpenVINO, CUDA)
• Understanding of modern Vision Transformers (ViT, DETR, Swin, TimeSformer, ViViT) and multimodal models (LLaVA, Qwen-VL)
• Experience with LLM, RAG, and agentic systems is a plus
• Experience in developing production AI systems and ML services
• DeepStream SDK
• CUDA
• Triton Inference Server
• Kafka
• Kubernetes
• LangGraph
• vLLM
• MLflow
• CUDA
• Triton Inference Server
• Kafka
• Kubernetes
• LangGraph
• vLLM
• MLflow
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