Generative AI & Machine Learning Engineer
About The Position
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. This is a Generative AI & Machine Learning Engineering position at the Vice President level, which is part of the job family responsible for developing and maintaining software solutions that support business needs. Morgan Stanley is an industry leader in financial services, known for mobilizing capital to help governments, corporations, institutions, and individuals around the world achieve their financial goals. 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.
Requirements
- 10+ years of AI/ML and software engineering experience, with a proven track record of designing, developing, and delivering production-grade AI solutions in enterprise environments.
- Proven experience leading engineering teams and delivering complex technology initiatives in large enterprise environments.
- Strong hands-on experience developing production-grade AI and machine learning applications.
- Deep experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and agent-based architectures.
- Strong programming skills in Python, with experience in Java or another enterprise programming language preferred.
- Experience developing distributed systems using microservices, REST APIs, containerization, and cloud-native architectures.
- Experience deploying AI applications using modern MLOps and DevOps practices.
- Strong understanding of software engineering fundamentals including system design, scalability, resiliency, testing, and performance optimization.
- Excellent communication skills with the ability to lead technical discussions across engineering and business teams.
- Experience working in Agile software development environments.
Nice To Haves
- Experience with OpenAI, Azure OpenAI, LangChain, LangGraph, or similar AI frameworks.
- Experience with vector databases and Retrieval-Augmented Generation (RAG) architectures.
- Experience building AI copilots, workflow automation, or agentic AI applications.
- Experience within Investment Banking, Capital Markets, or Financial Services technology.
- Experience with Kubernetes, Docker, GitHub Actions, Jenkins, MLflow, or similar DevOps and MLOps tooling.
- Familiarity with cloud platforms such as Azure, AWS, or Google Cloud
Responsibilities
- Lead the end-to-end design, development, and delivery of enterprise AI and machine learning solutions from concept through production deployment.
- Architect scalable, secure, and resilient AI platforms leveraging LLMs, Retrieval-Augmented Generation (RAG), intelligent agents, and modern machine learning techniques.
- Provide hands-on technical leadership during solution design, implementation, code reviews, and production support.
- Drive technical decision-making to ensure solutions are scalable, maintainable, and aligned with enterprise engineering standards.
- Collaborate closely with product owners, business stakeholders, architects, and engineering teams to translate business requirements into high-quality technical solutions.
- Lead technical planning, estimation, sprint execution, and delivery across multiple concurrent initiatives.
- Ensure AI solutions are production-ready with appropriate monitoring, observability, testing, security, and operational support.
- Drive engineering best practices including CI/CD, automated testing, code quality, infrastructure automation, and MLOps.
- Evaluate emerging AI technologies and recommend practical adoption where they improve delivery or engineering productivity.
- Mentor engineers and promote engineering excellence through technical guidance, design reviews, and knowledge sharing.
Benefits
- Ample opportunity to move about the business for those who show passion and grit in their work.
- Attractive and comprehensive employee benefits and perks in the industry.
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What This Job Offers
Job Type
Full-time
Career Level
Manager
Education Level
No Education Listed
