Senior AI Engineer

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 Software Engineering position at the Director 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. Interested in joining a team that’s eager to create, innovate and make an impact on the world? Read on.

The Firm Market Risk Technology Team develops software to measure and monitor Market Risk and Capital for the Firm’s global portfolio. We are responsible for designing and implementing technology solutions that calculate and assess the Market Risk of the Firm. Our systems support daily risk simulations and analytics that estimate potential losses across different time horizons.

We work closely with Risk Managers, Market Risk Analytics teams, Senior management, and various regulatory bodies globally. The Market Risk team constantly updates its current suite of risk management tools, frameworks and processes or builds new ones to meet frequently changing business and regulatory landscape.

As a Market Risk Technology Director, you will lead the development of Python-based AI applications and frameworks that empower business teams to onboard their own datasets and seamlessly leverage advanced tooling. Your solutions will provide a platform for intuitive data access, AI‑driven insights and automation working closely with the Market Risk business teams to help gain productivity and make faster, smarter decisions.

You will design and implement scalable solutions for risk data processing, RESTful services, and agentic workflows that support “talk to data” interfaces and Generative AI driven commentary. You will own technical direction, architectural decisions, and be accountable for production, reliability, and evolution of AI systems used by Market Risk teams. This is a hands‑on role requiring strong technical expertise in Python, Gen‑AI and modern engineering practices.

• Build and own Python-based AI applications leveraging natural language interfaces, Large Language Models, data analytics, and automation.
• Deliver innovative applications and tools that allow business users onboard and manage their datasets and leverage self‑service analytics.
• Architect and manage RESTful services for large‑scale enterprise solutions using open standards.
• Optimize data ingestion and processing pipelines for efficiency and performance.
• Collaborate with Firm Risk Management stakeholders to understand requirements and deliver AI solutions.
• Provide technical leadership and mentorship to developers within the agile squad.

• Strong hands‑on application development experience in Python, including building service APIs using frameworks such as FastAPI or Flask.
• Proven experience designing and delivering production‑grade AI or LLM‑based applications.
• Solid understanding of data engineering fundamentals including SQL, data modeling and data lifecycle management.
• Hands‑on experience with concurrency and performance optimization (multiprocessing, multithreading, asynchronous I/O).
• Practitioner of unit testing, integration and performance testing.
• Experience owning systems in production, including reliability, incident management, and continuous improvement.
• Ability to set technical direction, make architectural decisions, and mentor senior engineers.

• Experience with agentic workflows, natural language or “talk‑to‑data” systems.
• Hands‑on experience with Open Telemetry and observability tooling (Grafana, Prometheus).
• Experience with cloud‑native architectures, including Docker and Kubernetes.
• Familiarity with data streaming, messaging, or caching technologies (Kafka, Redis).
• Exposure to CI/CD, Dev Ops, or Git Ops practices (e.g., Jenkins, Git Hub Actions).