Data Scientist (Computer Vision)

ScryAI; Gurugram District; On-site; INR 1,000,000 - 2,000,000; Full time

Job summary

ScryAI is looking for a skilled individual with strong experience in computer vision and deep learning. You will work on object detection, classification, and real-time video analytics by building efficient pipelines and deploying CV models on edge devices.

The ideal candidate has hands-on experience with frameworks like PyTorch or TensorFlow, and familiarity with video analytics concepts. Your work will contribute significantly to real-world applications in smart cities and industrial monitoring.

Qualifications

• Strong hands-on experience with deep learning-based computer vision including object detection and real-time video analytics.
• Practical experience with CNN architectures such as YOLO and ability to train models using PyTorch or TensorFlow.
• Experience building real-time vision pipelines for live video feeds with low-latency constraints.

Skills

Deep learning

Computer vision

Object detection

Model optimization

Video analytics

Tools

PyTorch

TensorFlow

OpenCV

NVIDIA Jetson

Docker

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.