Ai Solutions Engineer

Morgan Stanley

Base: $150,000 - $210,000 py; bonus/equity: commis...
Generative ai (genai)
Large language models (llms)
Natural language processing (nlp)
The Firmwide Data Office is building an Enterprise Knowledge Graph using Graph, Semantic technologies, LLMs, and Agentic AI to map complex business, application, data, and infrastructure asset relationships

Job Summary

  • The Firmwide Data Office is building an Enterprise Knowledge Graph using Graph, Semantic technologies, LLMs, and Agentic AI to map complex business, application, data, and infrastructure asset relationships.
  • This role involves designing, architecting, and optimizing data-intensive systems, leveraging LLMs with large volumes of structured and unstructured data, and building/integrating Knowledge Graphs and Multiagent systems.
  • Morgan Stanley offers a supportive and empowering environment with opportunities for growth, comprehensive employee benefits, and a commitment to diversity and inclusion.

Matching Summary

The Firmwide Data Office is building an Enterprise Knowledge Graph using Graph, Semantic technologies, 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)
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • Knowledge Graph integration
  • Prompt Engineering
  • Retrieval Augmented Generation (RAG)
  • Vector Databases

Nice-to-have

  • Collaborative environment
  • Problem-solving mindset
  • Continuous learning
  • Diversity of thought

Key Requirements

  • Master's or PhD in Computer Science, Mathematics, Engineering, Statistics or related field
  • 5+ years' experience in traditional AI methodologies
  • Proven experience building and deploying GenAI models to production
  • Strong proficiency in Python with deep experience using frameworks like Pandas, PySpark, TensorFlow, XGBoost
  • Demonstrated experience dealing with big-data technologies
  • Experience designing and architecting high-performance, data-intensive systems
  • Strong understanding of multiagent architectures

Work Rights

Not specified

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