Data Scientist (Computer Vision)

ScryAI; Gurugram District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

ScryAI in Gurugram, India, seeks a senior computer vision engineer specializing in real-time video analytics and edge deployment to build end-to-end CV pipelines. You will work on CNN architectures like YOLO, train and optimize models with PyTorch or TensorFlow, and deploy on devices such as NVIDIA Jetson, ensuring low latency and robust performance across diverse lighting and camera setups.

You will collaborate with backend/DevOps teams, applying MLOps practices to monitor models in production

Qualifications

• Hands-on experience with deep learning-based CV for real-time video analytics.
• Proficient with CNNs (YOLO v5/v8) and training with PyTorch or TF.
• Edge deployment on devices like NVIDIA Jetson or Raspberry Pi.
• Experience building real-time vision pipelines with low-latency constraints.

Skills

Deep learning

Computer vision

Object detection

Video analytics

CNN architectures

YOLOv5/v8

PyTorch

TensorFlow

Edge deployment

MLOps

Docker

OpenCV

GStreamer

ONNX

TensorRT

OpenVINO

LangChain

LLM orchestration

Tools

Docker

OpenCV

GStreamer

ONNX

TensorRT

OpenVINO

LangChain

Job description

• 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.