Machine Learning Engineer

Responsibilities:

• Develop and optimize real-time computer vision applications for video analytics and edge AI solutions.
• Design and implement data pipelines to handle high-volume, real-time video streams.
• Build scalable, high-performance video processing pipelines with integration into cloud services or on-prem infrastructure.
• Collaborate with machine learning engineers to deploy deep learning models for object detection, segmentation, tracking, and classification on live video feeds.
• Ensure system performance, scalability, and robustness through optimization and efficient resource utilization.
• Participate in the full software development lifecycle, from requirements gathering to deployment and maintenance.
• Stay updated on the latest advancements in computer vision, machine learning, and deep learning technologies, incorporating them into the project roadmap.

Requirements:

• 3+ years of experience in computer vision or related fields.
• Expertise in NVIDIA DeepStream SDK for real-time video analytics and AI-based applications.
• Hands-on experience with Apache Kafka for building real-time data pipelines.
• Extensive experience in AWS
• Proficiency in OpenCV, GStreamer for image and video processing tasks.
• Strong experience with C++, Python, and libraries/frameworks such as
• TensorFlow, PyTorch, or TensorRT for deploying AI models.
• Understanding of video compression standards (H.264, H.265) and streaming protocols (RTSP, RTP, etc.).
• Familiarity with Docker and Kubernetes for deploying scalable microservices in cloud environments.
• Experience with Edge AI hardware like NVIDIA Jetson or similar platforms.
• Solid understanding of parallel computing and GPU acceleration techniques (CUDA).
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.