Assistant Vice President Data Science and Gen AI

• Design, Develop and deploy generative models for various applications in Fraud Operations.
• RAG Frameworks – Able to customize and fine-tune existing RAG frameworks or design new RAG to meet project requirements.
• Design and Develop POC / full scale solutions on various automations in Operations area including Conversational AI, Muti Agent Systems for multiple services.
• Responsible for development and deployment of Machine Learning / Deep Learning based models 
• Collaborate with cross-functional team to understand business requirements and translate them into AI solutions.
• Conduct research to advance the state-of-the art in generative modeling and stay up to date with latest advancements in the field.
• Optimize and fine-tune models for performance, scalability, and robustness.
• Implement and maintain AI pipelines and infrastructure to support model training and deployment.
• Perform data analysis and preprocessing to ensure high-quality input for model training.
• Implement and maintain AI pipelines and infrastructure to support model training and deployment. - Perform data analysis and preprocessing to ensure high-quality input for model training.
• Mentor junior team members and provide technical guidance. 
• Write and maintain comprehensive documentation for models and algorithms.
• Strong understanding of Model Risk Management (MRM) and Fair Lending (FL) guidelines for LLM based solutions
• Present findings and project progress to stakeholders and management. 

Recommended Qualifications:

• Bachelor’s or Master’s degree in computer science, Data Science, Machine Learning, or a related field. A Ph.D. is a plus. 
• 8+ years of experience in machine learning and deep learning, with a focus on generative AI solution design and development. 
• Strong proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras. 
• Strong background of Prompt Engineering, building agentic AI / AI Agents, API / MCPs for large scale automations in banking domain
• Experience with natural language processing (NLP) and natural language generation (NLG). 
• Proven track record of building and deploying generative models based solutions in production environments particularly using RAG frameworks.
• Solid understanding of machine learning algorithms, data structures, and software engineering principles. 
• Excellent problem-solving skills and the ability to work independently and as part of a team.
  • Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.