Requirements:

Retrieval-Augmented Generation (RAG): Experience in developing and implementing RAG models to enhance information retrieval and generation tasks.
Vector Stores: Knowledge of Vector Stores for efficient data storage and retrieval.
Prompt Engineering: Skills in designing and optimizing prompts for AI models to improve accuracy and relevance.
Large Language Model APIs (LLM APIs): Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Claude).
Programming Languages: Proficiency in Python, Java, or other relevant programming languages.
Data Analysis: Strong analytical skills and experience with data analysis tools.
Problem-Solving: Excellent problem-solving abilities and attention to detail.
Communication: Strong verbal and written communication skills.

Nice to Haves:

Graph RAG: Proficiency in using Graph RAG for complex data relationships and insights.
Knowledge Graphs: Expertise in building and managing Knowledge Graphs to represent and query complex data structures.
Machine Learning Frameworks: Experience with TensorFlow, PyTorch, or similar frameworks.
Experience with cloud platforms such as AWS, Google Cloud, or Azure.
Familiarity with natural language processing (NLP) and computer vision technologies.
Previous experience in a similar role or industry.
Master’s or Ph.D. in Computer Science, Data Science, or a related field.

Work with stakeholders to understand requirements and deliver AI solutions across several domains in Wealth Management.
Stay updated with the latest advancements in AI and machine learning technologies.
Conduct research and experiments to improve AI capabilities within the division.