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Morgan Stanley
The role involves building and deploying scalable AI agents and autonomous systems that utilize LLMs to complete multi-step tasks. The engineer will collaborate across global teams to operationalize distributed data flows and ensure robust, agile architecture for AI solutions.
Candidates must have over 15 years of IT experience, with at least 10 years specifically focused on ML and GenAI solution architecture and development. Proficiency in Python, agent orchestration frameworks, and MLOps is mandatory for this senior-level position.
Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management, and wealth management services. The Firm's employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices. As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career - a place for people to learn, to achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture.
Technology works as a strategic partner with Morgan Stanley business units and the world's leading technology companies to redefine how we do business in ever more global, complex, and dynamic financial markets. Morgan Stanley's sizeable investment in technology results in quantitative trading systems, cutting-edge modeling and simulation software, comprehensive risk and security systems, and robust client-relationship capabilities, plus the worldwide infrastructure that forms the backbone of these systems and tools. Our insights, our applications and infrastructure give a competitive edge to clients' businesses—and to our own.
This role will 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.
Responsibilities:
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 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
Primary Skills / Qualifications:
Experienced professional with overall 15+ years of IT experience… out of which specifically 10+ years of experience working towards building ML & GenAI solutions & supporting components design, architecture, development, and operationalization and agent orchestration 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.
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
WHAT YOU CAN EXPECT FROM MORGAN STANLEY:
At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Expected base pay rates for the role will be between $195,000 and $275,000 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.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.
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Base Salary (from JD)
$195,000 – $275,000
AI Est. Total Comp
$435,000
Location
New York
Work Type
On-site
Seniority
executive
Experience
10+ years
Category
ml ai
Quality Score
8.0