GenAI Strats - Fixed Income - Vice President

Morgan Stanley
New York, New York, United States of America2026-05-06Full time

About the job

We are seeking a passionate, collaborative VP-level GenAI professional to help shape how Generative AI is applied across financial and business workflows. This is a high-visibility opportunity to work at the intersection of markets, data, software engineering, and applied AI - building tools that go beyond prototypes and deliver measurable business impact.

Responsibilities

Translate business, data, and workflow problems into structured solutions and drive delivery end to end.

Design, build, and deploy AI-powered applications, models, dashboards, and automation tools.

Partner with trading, sales, risk, finance, operations, and technology teams to identify priorities and deliver measurable impact.

Apply strong engineering discipline, including testing, version control, monitoring, and iteration based on feedback and usage.

Communicate technical concepts and analytical findings clearly to both technical and non-technical stakeholders.

Qualifications

Minimum

VP-level judgment, ownership, and the ability to independently lead workstreams from problem framing through delivery.

Strong communication skills and comfort working across business and technology teams.

Strong Python skills, familiarity with the data science stack, and solid software engineering fundamentals.

Experience with Generative AI / LLMs, including prompt design, evaluation, retrieval or tool use, and application development.

Familiarity with SQL, APIs, and structured or time-series data.

A portfolio of projects, prototypes, published work, or open-source contributions that demonstrate how you frame problems and build solutions; GitHub repositories and public examples are encouraged.

Preferred

Experience in financial services or the ability to quickly learn financial products, market structure, and business workflows.

Exposure to fixed income, rates, derivatives, pricing, trade lifecycle, or control-oriented workflows is a plus.

Quantitative background in computer science, engineering, mathematics, statistics, physics, or a related field.