AI Enginner

We are looking for a Video Analysis AI Engineer with a strong foundation across the complete software stack — from ingesting video streams to processing, analysing, and visualising results.

Key Responsibilities

Video & Data Processing

Building and maintaining video processing and analysis pipelines

Work with live and file‑based video streams (IP video, encoded streams)

Support computer vision / video analytics tasks such as detection, metadata extraction, tagging, and quality checks

Software Stack Development

Develop and maintain backend services for video analysis workflows

Work on AI based video analysis and Metadata development

Work with APIs, data stores, and processing jobs supporting AI / ML based analytics outputs

Assist in integrating analytics results with dashboards, logs, or reporting tools

Systems & Operations

Help deploy and test applications in Linux-based and containerised environments

Assist in debugging issues across the stack — ingestion, processing, storage, and outputs

Support monitoring, logging, and performance analysis for live systems

Collaboration & Learning

Work with Junior/Intern engineers to mentor production video systems

Document implementations and learn best practices for scalable media platforms

Participate in offshore team's technical work, code reviews and technical discussions

Required Skills & Qualifications

Core Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field

3–5 years of experience in relevant field and technologies.

2+ years of hands-on working experience in Linux environments

Hands-on experience in C, C++, Java and Python (mandatory)

Strong working experience in backend frameworks, scripts, or microservices and databases (SQL and/or NoSQL)

Exposure to video fundamentals (codecs, frames, resolution, bitrates)

Strong knowledge of computer vision or video analytics concepts

Familiarity with libraries such as OpenCV, FFmpeg, or similar is a plus

Exposure to machine learning frameworks (PyTorch, TensorFlow, ONNX), containers (Docker) or cloud platforms

Experience with real-time data or streaming systems