Partner with the Advanced Analytics, Machine learning and Gen AI Platform team(s), across multiple project areas, and work in collaboration with team(s) in India & US. The individual would be responsible for building autonomous systems that can reason, use tools, and complete multi-step tasks using LLMs/Reasoning models, build calibration scoring and guardrails for agent accuracy. The person would also be part of the overall cloud adoption and engineering roadmap and ensure scalable, agile and robust architecture and implementation. Additionally, should be able to work in a dynamic environment with limited or no supervision and should be able to knowledge share across other team members. Should be comfortable and manage time working with global team on multiple initiatives.
What you'll do in the role:
- Sr Hand-On Engineer who acts as the catalyst in building and deploying AI Agents at scale to accelerate technology and business roadmaps
- Collaborate across multiple peer divisions to get alignment on AI Solutions demonstrate with POCs the "Art of the possible" and accelerate adoption
- Evaluate state-of-art 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
What you'll bring to the role:
- Experienced professional with 6+ years of experience working towards building GenAI solutions & supporting components design, architecture, development, and operationalization of agent orchestrations at scale.
- 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.
- Ability to work in Fast paced and Dynamic environment.
- Good written and verbal communication skills
Expected base pay rates for the role will be between $150,000 and $215,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
