Job Summary :

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.

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.

Technical Skills :

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).

Soft Skills :

- Strong analytical and problem-solving skills.

- Ability to work independently and in a fast-paced team environment.

- Excellent communication and documentation abilities.

- Eagerness to learn and adapt to new tools and technologies.

Educational Qualification :

- Bachelor's or Masters degree in Computer Science, Electronics, AI/ML, or related field