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- Develop, optimize, and maintain object detection models using Python and relevant ML frameworks (e.g., TensorFlow, PyTorch).
- Design and implement end-to-end pipelines for data preprocessing, model training, evaluation, and deployment.
- Collaborate with cross-functional teams to understand project requirements and deliver tailored solutions.
- Enhance the accuracy and efficiency of object detection systems by experimenting with innovative techniques and architectures (e.g., YOLO, Faster R-CNN, SSD).
- Integrate object detection models into scalable applications, ensuring seamless interaction with APIs and external systems.
- Perform data analysis and visualization to guide decision-making and assess model performance.
- Identify, debug, and resolve technical challenges in machine learning workflows.
- Stay updated with the latest advancements in object detection, computer vision, and deep learning.
Required Skills and Qualifications:
- Programming:Â Proficiency in Python, with a strong focus on writing clean, maintainable code.
- Machine Learning Frameworks:Â Hands-on experience with TensorFlow, PyTorch, Keras, or similar libraries.
- Object Detection:Â Expertise in implementing and optimizing object detection models such as YOLO, Faster R-CNN, SSD, or similar architectures.
- Data Handling:Â Proficiency in handling large datasets, including data cleaning, transformation, and augmentation.
- Algorithms & Mathematics:Â Strong understanding of machine learning algorithms, computer vision techniques, and statistical modeling.
- Deployment Tools:Â Experience with deploying models using tools such as Docker, Flask, FastAPI, or cloud platforms like AWS, Azure, or Google Cloud.
- Version Control:Â Proficient in Git for collaborative development.
- Problem-Solving:Â Strong analytical skills with the ability to tackle challenging technical problems.
How to Apply:
- First, read through all of the job details on this page.
- Scroll down and press the Click Here button.
- To be redirected to the official website, click on the apply link.
- Fill the details with the information provided.
- Before submitting the application, cross-check the information you’ve provided.