AI Solution Architect

We're seeking someone to join our team as an AI Solution Architect who will help business teams move from AI ideas to well-scoped, testable MVP prototypes by supporting use case discovery, prompt engineering, experimentation, stakeholder coordination, and feasibility assessment. The role serves as a bridge between business and technology, explaining AI capabilities in simple terms while translating business problems into clear technical requirements. The role is well suited for someone who can operate comfortably across both domains and help maintain momentum from idea through prototype validation.

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 an AI Solution Architect position which is part of the job family responsible for analyzing business processes, identifying needs, and establishing requirements to enhance efficiency and productivity through a blend of business acumen and analytical skills.

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

What you'll do in the role:

• Partner with stakeholders to identify workflows, pain points, and high-value AI opportunities.

• Translate ambiguous ideas into clear use cases, requirements, and MVP prototype plans.

• Support prompt engineering, workflow design, and AI solution prototyping.

• Design agentic workflows that combine model reasoning, tools, data, and human review.

• Run evaluations to test prompt quality, model behavior, workflow feasibility, and user experience.

• Assess prototype value, feasibility, risks, limitations, and readiness for investment.

• Coordinate across business users, technology teams, governance partners, and Forward Deployed Engineers.

• Prepare use cases for delivery by documenting scope, data needs, dependencies, risks, and business context.

• Capture reusable patterns, prompt examples, evaluation methods, and lessons learned to scale Applied AI enablement.

• Monitor emerging AI trends and technologies, and apply relevant developments to testing and evaluation approaches.

• Proficiency using AI tools such as ChatGPT, Claude, Copilot, or similar platforms.

• Strong understanding of generative AI concepts, including agents, prompts, context, model outputs, hallucination risk, and evaluation.

• Systems thinking, with the ability to understand how people, processes, data, and technology connect.

• Ability to explain technical concepts clearly to business audiences.

• Ability to translate business problems into structured technical requirements.

• Comfort working across business and technology teams.

• Strong problem-solving skills and comfort with ambiguity.

• Ability to manage multiple concurrent initiatives while maintaining clear priorities, stakeholder alignment, and delivery momentum.

• Interest in AI, agentic automation and innovation.

• Strong communication and stakeholder management skills.