Key Responsibilities:

• Collaborate with cross-functional teams including machine learning engineers, researchers, and domain experts to define and understand project requirements.
• Experience working with OCR models and generative AI agents.
• Design, and implement state-of-the-art generative models such as GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and other relevant techniques.
• Fine-tune generative models using large datasets to create realistic and high-quality synthetic data that mirrors real-world patterns.
• Develop and optimize algorithms to generate diverse and representative samples while maintaining data privacy and security.
• Work on improving the performance, stability, and scalability of existing generative models, making them suitable for production environments.
• Collaborate with the data engineering team to design pipelines for efficient data preprocessing, model training, and deployment.
• Evaluate the effectiveness and quality of generated data through quantitative and qualitative assessments, and iterate on models to achieve desired outcomes.
• Stay up-to-date with the latest advancements in generative modeling techniques and contribute to the company's knowledge base through research papers, presentations, and internal documentation.
• Participate in brainstorming sessions to identify new use cases and innovative applications of generative models across the company's products and services.

Qualifications:

• Master's or Ph.D. degree in Computer Science, Machine Learning, Data Science, or a related quantitative field.
• Proven experience working with generative models, such as GANs, VAEs, and other relevant techniques.
• Strong programming skills in languages such as Python, TensorFlow, and PyTorch for model development, training, and evaluation.
• Solid understanding of deep learning architectures, optimization techniques, and model evaluation metrics.
• Proficiency in working with large datasets and experience in data preprocessing, transformation, and augmentation.
• Knowledge of cloud platforms and tools for deploying machine learning models at scale is a plus.
• Excellent problem-solving skills, with the ability to research, analyze, and implement innovative solutions independently.
• Strong communication skills to collaborate effectively with cross-functional teams and present complex technical concepts to non-technical stakeholders.