Model Development and Optimization
- Design, develop, and optimize generative AI models using deep learning techniques, such as NLP, transformers, GANs, and reinforcement learning, to address specific business needs like personalized customer interactions, automated document processing, and predictive analytics.
- Continuously improve model performance by experimenting with various architectures and training methodologies.
- Design and implement AI applications using large language models (LLMs) and other generative AI technologies
- Develop and optimize prompts for various use cases to enhance model performance
- Build and maintain Retrieval Augmented Generation (RAG) systems to improve AI responses with external knowledge
- Implement Graph RAG solutions for complex knowledge structures
- Assist in model fine-tuning projects to adapt pre-trained models for specific use cases
- Collaborate with cross-functional teams to gather requirements and implement AI solutions
Integration and Deployment
- Collaborate with data engineers and software developers to deploy AI models into production environments, ensuring they are scalable, efficient, and secure.
- Work with DevOps teams to establish MLOps pipelines for continuous integration, deployment, and monitoring of AI models.
- Ensure the seamless integration of AI solutions with existing banking platforms and systems.
Compliance and Data Governance:
- Ensure that all AI models and solutions adhere to the bank's data privacy, security, and compliance standards, such as GDPR, AML, and KYC.
- Develop transparent, explainable AI (XAI) solutions that meet regulatory requirements and are aligned with ethical AI guidelines.
- Participate in model risk management processes, including validation and documentation.