The Role
Develop and deploy deep learning computer vision systems for real-time video analytics. Responsibilities include training and optimizing detection, classification, tracking, and video models; building end-to-end pipelines; deploying models to edge devices; integrating vision outputs with LLMs; monitoring production performance; and collaborating with backend and DevOps teams on containerized, low-latency deployments.
Summary Generated by Built In
Please refer to the Company Website - https://scryai.com/careers/?q=jobs
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.
Skills Required
- Hands-on experience with deep learning-based computer vision, including object detection, classification, tracking, and real-time video analytics.
- Experience with CNN architectures such as YOLO and training, fine-tuning, and evaluating models using PyTorch or TensorFlow.
- Experience building low-latency real-time vision pipelines for CCTV or streaming video.
- Understanding of video analytics concepts including frame sampling, motion analysis, temporal consistency, and object tracking.
- Understanding of image and video preprocessing, augmentation, normalization, and real-world data challenges.
- Hands-on experience deploying computer vision models on edge devices such as NVIDIA Jetson or Raspberry Pi.
- Experience with model optimization techniques including quantization, pruning, or lightweight architectures.
- Ability to design and own end-to-end computer vision pipelines from data ingestion and annotation through production monitoring and evaluation.
- Experience with Vision-Language Models or vision-enabled LLMs and integrating model outputs with LLM pipelines.
- Experience collaborating with backend and DevOps teams on production deployment, including Docker and basic MLOps practices.
- Ability to evaluate and monitor production model performance using computer vision metrics.
- Experience with edge inference frameworks such as ONNX, TensorRT, or OpenVINO.
- Experience with video streaming and processing frameworks such as OpenCV, RTSP, or GStreamer.
- Exposure to multimodal AI systems combining vision with text or audio.
- Experience with multi-camera setups, camera calibration, or scene-level analytics.
- Familiarity with LangChain or LlamaIndex.
- Understanding of edge AI security, privacy, and data compliance in surveillance or industrial environments.
- Experience with real-world computer vision deployments in smart cities, retail analytics, industrial monitoring, safety systems, or surveillance.
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The Company
What We Do
Scry AI is a research-led enterprise AI company that develops intelligent platforms for businesses in banking, financial services, insurance, logistics, and industrial sectors. Its suite includes Auriga for conversational AI, Collatio for document intelligence, and Concentio for cognitive IoT and operational intelligence. The platforms process fragmented data, automate document and workflow operations, support compliance, and generate actionable insights to improve enterprise efficiency.
