Job Description

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.