1. Model Development: Design, implement, and evaluate machine learning models to solve computer vision related problems or enhance existing systems.
2. Model Training and Optimization: Train machine learning models using appropriate algorithms and techniques, and optimize them for accuracy, efficiency, and scalability.
3. Evaluation and Validation: Assess model performance through rigorous testing, validation, ensuring reliability and generalization.
4. Performance Optimization: Optimize algorithms and models for speed, memory usage, and computational efficiency, especially for real-time applications on resource-constrained environments.
5. Deployment and Integration: Deploy models into production environments, integrate them with existing systems, and monitor their performance and behavior over time.
6. Collaboration and Communication: Work closely with cross-functional teams including data annotators, software engineers, and domain experts to understand requirements, gather feedback, and iterate on solutions.
7. Research and Development: Stay updated with the latest advancements in computer vision research and contribute to the development of innovative solutions.
8. Documentation and Reporting: Document all aspects of the machine learning pipeline, including data sources, preprocessing steps, model architecture, hyperparameters, and evaluation metrics. Produce clear and concise reports or presentations to communicate findings and insights effectively.

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1. Education: Bachelor’s or Master’s degree in Mathematics, Computer Science, or a related field. A strong background in mathematics, particularly linear algebra, calculus and Probability Theory is essential.
2. Experience: 3-5 years of hands-on experience in this field
3. Strong Background in Machine Learning: Solid understanding of machine learning concepts, algorithms, and techniques, including deep learning, computer vision etc.
4. Proficiency in Programming: Proficient in Python and experience with libraries like TensorFlow or PyTorch is highly desirable. Experience in C++ will be regarded as an additional advantage.
5. Software Engineering Skills: Familiarity with software engineering principles, best practices, and version control systems (e.g., Git) for developing robust, scalable, and maintainable code.
6. Problem-solving Abilities: Aptitude for identifying, formulating, and solving complex problems using machine learning and analytical techniques.
7. Communication and Collaboration: Excellent communication skills to articulate ideas, collaborate effectively with team members, and present findings to diverse audiences.

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