Computer vision

Computer Vision Engineer

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Position Overview:

We are looking for a skilled Computer Vision Engineer to develop and deploy AI-powered video analytics solutions, with a strong focus on video surveillance applications. This is a hands-on individual contributor role requiring deep expertise across the full ML lifecycle — from model development through to production deployment and monitoring. The engineer will work on real-time AI video analytics for surveillance systems, including object detection, tracking, behavioural analysis, and edge or cloud deployment of video inference pipelines. The selection process consists of a maximum of 2 interview rounds — a technical assessment and a culture fit conversation.

Key Responsibilities:

• Develop and deploy AI-powered video analytics solutions for real-time surveillance systems
• Build and maintain YOLO-based object detection pipelines
• Implement object detection, tracking, and behavioural analysis models
• Manage the full ML lifecycle including model development, testing, deployment, and monitoring
• Deploy video inference pipelines on edge or cloud infrastructure
• Work with DevOps tools including Docker, Kubernetes, and CI/CD pipelines
• Process and analyse real-time video streams
• Develop and integrate neural network architectures including CNN, RNN, and GAN variants
• Apply supervised and unsupervised ML algorithms to solve computer vision problems
• Monitor and maintain deployed models in production environments

Required Skills:

• YOLO — Strong hands-on experience with YOLO-based object detection pipelines (Mandatory)
• Python
• OpenCV
• TensorFlow
• Keras
• scikit-learn
• CNN (Convolutional Neural Networks)
• RNN (Recurrent Neural Networks)
• GAN (Generative Adversarial Networks)
• Supervised and unsupervised machine learning techniques
• MLOps — Model development, testing, deployment, and monitoring
• Docker
• Kubernetes
• CI/CD pipelines
• Minimum 3 years of experience in Computer Vision

Preferred (Bonus) Skills:

• Experience with real-time video stream processing
• Exposure to activity recognition or anomaly detection models
• Familiarity with ONVIF protocol
• Familiarity with RTSP protocol
• Experience with IP camera integration