We are working on an exciting new initiative to build an Enterprise Knowledge Graph by harnessing the power of Graph and Semantic technologies along with LLMs, and Agentic AI to map complex business, application, data, and infrastructure asset relationships
Job Summary
We are working on an exciting new initiative to build an Enterprise Knowledge Graph by harnessing the power of Graph and Semantic technologies along with LLMs, and Agentic AI to map complex business, application, data, and infrastructure asset relationships.
The ideal candidate, in addition to experience in data science, will possess expertise in designing, architecting, and optimising data-intensive systems, with a keen focus on big data analytics.
We embrace a culture of experimentation and constantly strive for improvement and learning.
Matching Summary
We are working on an exciting new initiative to build an Enterprise Knowledge Graph by harnessing the power of Graph and Semantic technologies along with LLMs, and Agentic AI to map complex business, application, data, and infrastructure asset relationships.
Salary
Base: $150,000 - $210,000 per year; Bonus/Equity: commission earnings, incentive compensation, discretionary bonuses, other short- and long-term incentive packages; Benefits: other Morgan Stanley sponsored benefit programs
Skills & Requirements
Must-have
Generative AI (GenAI) models
Large Language Models (LLMs)
Natural Language Processing (NLP)
Graph and Semantic technologies
Python with Pandas, PySpark, TensorFlow
Prompt Engineering and RAG
Vector Databases
Nice-to-have
Collaborative team environment
Innovative problem-solving
Continuous learning and improvement
Diversity of thought
Key Requirements
Master’s or PhD in Computer Science, Mathematics, Engineering, Statistics
5+ years’ experience in traditional AI methodologies
Proven experience building and deploying GenAI models to production
Demonstrated experience with big-data technologies
Experience designing scalable, reliable data-intensive systems