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Scry AI Linkedin · Posted 4mo ago

Data Scientist

India

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Indexed description

Requirements

  • 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 the 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.

This job was posted by Piyush Verma from Scry AI.
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