
Ai Ml Engineer
2 weeks ago
Job Summary:
We are looking for a skilled AI Video Analytics Engineer with 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.
Key Responsibilities:
- Design and implement AI solutions for real-time video analytics in surveillance environments.
- Develop and deploy object detection, tracking, and OCR models using YOLO, MobileNetSSD, and other state-of-the-art architectures.
- Optimize models for edge and server deployment using TensorRT and other acceleration frameworks.
- Integrate AI pipelines into video streams using GStreamer and FFmpeg.
- Handle real-time data exchange using protocols like HTTP, MQTT, and TCP/UDP.
- Build and consume RESTful APIs for model inference, alerts, and data flow.
- Collaborate with cross-functional teams to design scalable and efficient systems for POCs and production-grade solutions.
- Work with CCTV/RTSP/ONVIF streams for real-time monitoring and analytics.
- Ensure performance tuning and latency optimization for real-time use cases.
Must-Have:
- Strong experience in Object Detection, Tracking, and OCR using deep learning.
- Proficiency with TensorFlow, PyTorch, and TensorRT.
• Hands-on experience with YOLO (v5/v8), MobileNet SSD, or similar architectures. - Solid knowledge of video streaming frameworks GStreamer, FFmpeg.
- Experience with real-time communication protocols HTTP, MQTT, TCP/UDP.
- Working knowledge of REST API development and consumption.
- Exposure to OpenCV, NumPy, and other computer vision libraries.
- Understanding of AI deployment pipelines for edge and cloud.
Good to Have:
• Experience with ONVIF, RTSP camera integrations.
• Exposure to MLOps tools and practices.
• Familiarity with containerization (Docker) and deployment pipelines (CI/CD).
• Basic knowledge of cloud services (AWS, GCP, Azure).
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