CMS Info Systems - AI/ML Engineer - Image Processing/Deep Learning

<p><p><b>Job Summary : </b><br/><br/>We are looking for a skilled AI Video Analytics Engineer with 34 years of hands-on experience in AI-based surveillance, object detection, tracking, and OCR.

The ideal candidate will have a strong background in deep learning frameworks (TensorFlow, PyTorch), video processing (GStreamer, FFmpeg), and practical deployment of models using optimization tools like TensorRT.

Experience in working with real-time communication protocols and multimedia systems is essential.<br/><br/><b>Key Responsibilities : </b><br/><br/>- Design and implement AI solutions for real-time video analytics in surveillance environments.<br/><br/>- Develop and deploy object detection, tracking, and OCR models using YOLO, MobileNetSSD, and other state-of-the-art architectures.<br/><br/>- Optimize models for edge and server deployment using TensorRT and other acceleration frameworks.<br/><br/>- Integrate AI pipelines into video streams using GStreamer and FFmpeg.<br/><br/>- Handle real-time data exchange using protocols like HTTP, MQTT, and TCP/UDP.<br/><br/>- Build and consume RESTful APIs for model inference, alerts, and data flow.<br/><br/>- Collaborate with cross-functional teams to design scalable and efficient systems for POCs and production-grade solutions.<br/><br/>- Work with CCTV/RTSP/ONVIF streams for real-time monitoring and analytics.<br/><br/>- Ensure performance tuning and latency optimization for real-time use cases.<br/><br/><b>Technical Skills : </b><br/><br/><b>Must-Have : </b><br/><br/>- Strong experience in Object Detection, Tracking, and OCR using deep learning.<br/><br/>- Proficiency with TensorFlow, PyTorch, and TensorRT.<br/><br/>- Hands-on experience with YOLO (v5/v8), MobileNet SSD, or similar architectures.<br/><br/>- Solid knowledge of video streaming frameworks GStreamer, FFmpeg.<br/><br/>- Experience with real-time communication protocols HTTP, MQTT, TCP/UDP.<br/><br/>- Working knowledge of REST API development and consumption.<br/><br/>- Exposure to OpenCV, NumPy, and other computer vision libraries.<br/><br/>- Understanding of AI deployment pipelines for edge and cloud.<br/><br/><b>Good to Have : </b><br/><br/>- Experience with ONVIF, RTSP camera integrations.<br/><br/>- Exposure to MLOps tools and practices.<br/><br/>- Familiarity with containerization (Docker) and deployment pipelines (CI/CD).<br/><br/>- Basic knowledge of cloud services (AWS, GCP, Azure).<br/><br/><b>Soft Skills : </b><br/><br/>- Strong analytical and problem-solving skills.<br/><br/>- Ability to work independently and in a fast-paced team environment.<br/><br/>- Excellent communication and documentation abilities.<br/><br/>- Eagerness to learn and adapt to new tools and technologies.<br/><br/><b>Educational Qualification : </b><br/><br/>- Bachelor's or Masters degree in Computer Science, Electronics, AI/ML, or related field</p><br/></p> (ref:hirist.tech)