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Data Scientist
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Must Have:
- Strong hands-on experience with deep learning-based computer vision, including object detection, classification, tracking, and real-time video analytics
- Practical experience with CNN-based architectures such as YOLO (v5/v8) or similar, and ability to train, fine-tune, and evaluate models using PyTorch or TensorFlow
- Experience building real-time vision pipelines for live video feeds (CCTV / streaming video) with low-latency constraints
- Solid understanding of video analytics concepts including frame sampling, motion analysis, temporal consistency, and object tracking across frames
- Strong understanding of image and video preprocessing pipelines including augmentation, normalization, and handling real-world data challenges such as low light, occlusion, motion blur, and varying camera angles
- Hands-on experience deploying CV models on edge devices such as NVIDIA Jetson, Raspberry Pi, or similar embedded platforms
- Exposure to model optimization techniques for edge deployment including quantization, pruning, or use of lightweight architectures
- Ability to design and own end-to-end CV pipelines, from data ingestion and annotation to inference, monitoring, and performance evaluation in production
- Experience working with Vision-Language Models (VLMs) or vision-enabled LLMs, and integrating vision model outputs with LLM pipelines for reasoning, event understanding, or summarization
- Experience collaborating with backend and DevOps teams for production deployment, including familiarity with Docker and basic MLOps practices
- Ability to evaluate and monitor model performance in production using appropriate computer vision metrics
Good to Have:
- Experience with edge inference frameworks such as ONNX, TensorRT, or OpenVINO
- Hands-on experience with video streaming and processing frameworks such as OpenCV, RTSP, GStreamer, or similar
- Exposure to multimodal AI systems combining vision with text (and optionally audio)
- Experience with multi-camera setups, camera calibration, or scene-level analytics
- Familiarity with LLM orchestration frameworks such as LangChain or LlamaIndex
- Understanding of edge AI security, privacy, and data compliance considerations in surveillance or industrial environments
- Experience working on real-world CV deployments in domains such as smart cities, retail analytics, industrial monitoring, safety systems, or large-scale surveillance.
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