Responsibilities:
- Design and implement end-to-end machine learning workflows for image and computer vision applications, from data collection to model deployment.
- Collaborate with cross-functional teams, including data engineers, product managers, and domain experts, to define and prioritize machine learning initiatives.
- Document technical designs and model specifications, ensuring clarity and accessibility for stakeholders and team members.
- Ensure adherence to best practices in model development, deployment, and monitoring, in alignment with the overall AI strategy.
- Monitor model performance and implement strategies for continuous improvement and retraining as needed.
- Develop scalable and efficient deep learning models using PyTorch, optimizing for performance and resource utilization.
Qualifications:
- Bachelors degree in Computer Science or a related field.
- 4-7 years of hands-on experience in developing and deploying machine learning models, particularly in computer vision tasks.
- Proficient in using PyTorch for developing deep learning models, with a strong understanding of CNNs, transfer learning, vision transformers, and data augmentation techniques.
- Solid understanding of computer vision concepts, including image classification, object detection, and image segmentation.
- Strong programming skills in Python, with experience in data manipulation libraries such as NumPy and Pandas.
- Experience with version control systems like Git.
- Excellent analytical and problem-solving skills, strong communication abilities, and a collaborative mindset.
Preferred Qualifications:
- Experience with cloud platforms (e.g., AWS, GCP, Azure) and their ML services, particularly those related to model deployment and GPU training.
- Understanding of MLOps principles and practices, including model monitoring, versioning, and governance.
- Knowledge of GPU computing and tools for managing GPU resources (e.g., CUDA, cuDNN).
