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Tier-1 brand, metro location, and mid-level experience create high applicant competition.
Strong bias toward GPU-accelerated AI, computer vision, and embedded validation reduces cross-industry transferability.
Requires 5+ years plus specific AI/embedded, Python, Docker, Kubernetes, and GPU experience.
Develop and enhance Python-based test frameworks and CI/CD automation for validating end-to-end intelligent video analytics (IVA) and AI workflows across cloud, data center, workstation, and edge devices.
Build and execute functional, integration, system, and end-to-end validations for video AI applications including multi-camera, multi-stream, and simulation workflows spanning video processing to AI inference and visualization.
Leverage AI-powered tools and agents to accelerate test generation, automation, debugging, coverage improvement, and analysis across distributed GPU and containerized environments using Docker, Kubernetes, and Helm.
B.Tech or M.Tech degree in Computer Science, Computer Engineering, IT, Electronics, or equivalent experience.
5+ years of hands-on software test development or automation experience preferably in AI/ML, computer vision, video analytics, embedded systems, or GPU-accelerated applications.
Strong Python programming skills with experience building test frameworks and automation tools from scratch.
Proficiency in Linux including shell scripting, system-level debugging, CI/CD experience, and familiarity with Docker and containerized deployments.
Experienced in AI/ML and computer vision concepts such as object detection, classification, tracking, video analytics, and AI inference pipelines.
Knowledgeable in distributed microservices-based AI architectures, video streaming, and validation on GPU platforms including NVIDIA Metropolis, DeepStream, TensorRT, CUDA stack.
Demonstrated ability to use AI-powered development and test tools to meaningfully improve test automation quality, coverage, and regression efficiency in complex AI software systems.