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Job Description

Job Description :

Key Responsibilities :


Model Deployment & Integration :


- Convert trained models to deployment formats (e.g., ONNX, TensorRT, OpenVINO, CoreML).


- Integrate models/algorithms provided by ML engineers into applications.

- Collaborate with Computer Vision ML engineers and testing/DevOps for production rollout.

Performance Optimization :


- Reduce latency and memory use in VA pipeline


- Optimizing and Inferencing of models

- Optimizing the code for different hardware : CPU, GPU and specialized devices, on linux and

Windows operating systems.

- Application-Level Logic & CV Algorithm Implementation

- Creating custom business logic as per clients requirement

- Implementing image processing and computer vision algorithms for diverse use cases.

Pipeline Engineering :


- Building real-time or batch inference pipelines (using frameworks like Opencv or Gstreamer).


Infrastructure & Monitoring :


- Track status of model inference time and memory..


- Monitor model performance, latency, and hardware utilization.

- Implement CI/CD for model updates.

Required Skills :


- High proficiency in C, C++

- Strong understanding of image processing, computer vision algorithms and machine learning

- High competency with image processing, computer vision and machine learning libraries such as OpenCV, Keras, OpenVino, Deepstream, Pytorch, Tensorflow

- Familiarity with Docker for containerization and deployment of applications.

- Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

Preferred skills :

- Familiarity with DeepStream for real-time video analytics and processing.

- Experience with OpenVINO toolkit for optimizing and deploying deep learning models on Intel hardware.


- Knowledge of ONNX Runtime for inference of models trained in popular deep learning frameworks.

- Experience with GStreamer for multimedia handling and streaming.


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