Data Scientist (Computer Vision) (Hyderabad)

Worksite: Noida, Gurgaon, Pune, Hyderabad 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 computer vision (CV) models on edge devices such as NVIDIA Jetson, Raspberry Pi, and similar embedded platforms - Exposure to model optimization techniques for edge deployment, including quantization, pruning, and 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, integrating vision outputs into LLM pipelines for reasoning, event understanding, and 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 Valuable 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 .