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Morgan Stanley
AI / ML Engineer
Morgan Stanley
New York•On-site•2-5 Yrs•Jun 29
Job description
Overview
In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities.
Responsibilities
- Hands-On Engineer who acts as the catalyst in building and deploying AI Agents at scale to accelerate technology and business roadmaps
- Evaluate state-of-art ML and Gen AI centric technologies and prototype solutions to improve our architecture and platform
- Design, Implement and Operationalize distributed, scalable, and reliable data flows that ingest, process, store, and access data at scale in batch / real-time used by AI Agents
Qualifications
- Agent Orchestration Frameworks: Mastery of frameworks like LangChain, LangGraph, CrewAI, and Microsoft's AutoGen to build multi-agent workflows.
- LLM Implementation: Deep expertise in LLM APIs (OpenAI, Anthropic, AWS Bedrock), prompt engineering (Chain-of-Thought), and fine-tuning for specific agentic behaviors.
- Memory & Context Management: Ability to implement complex memory systems, including vector databases (Pinecone, Weaviate) and Retrieval-Augmented Generation (RAG) pipelines.
- Tool-Calling & APIs: Proficiency in integrating external APIs as agent tools, including function calling and error handling for malformed model outputs.
- Languages & Backend: Expert-level Python (mandatory), often paired with FastAPI, Node.js, or Go. Familiarity with Docker and Kubernetes for containerized deployment is standard.
- Understanding of applied Machine Learning (End-to-End) Lifecycle and Operationalizing ML models in Production (MLOps)
- Ability to work in Fast paced and Dynamic environment.
- Good written and verbal communication skills