Research - Vice President, Research Technology Management (Lead Prompt Engineer) (Mumbai)

Vice President, AI Enablement Lead

Research Technology Management | Global Research | Morgan Stanley

Position Overview

Morgan Stanley Global Research is seeking a Vice President to serve as AI Enablement Lead within the Research Technology Management (RTM) Asia-Pacific  The AI Enablement Lead will be responsible for driving the practical adoption, development, governance, and optimization of generative AI and related AI capabilities across Global Research’s Asia-Pacific and Japan division.

The role will operate at the intersection of Research, technology, data, AI/modeling, product management, training, and governance.

The AI Enablement Lead will be responsible for translating the needs of Asia Pacific and Japan Research analysts and teams into scalable, compliant, high-value AI solutions. The role will oversee the complete enablement lifecycle—from identifying and prioritizing use cases through requirements definition, solution development, evaluation, deployment, adoption, measurement, and ongoing support.

The successful candidate will combine a strong understanding of financial services and investment research workflows with hands-on AI and technical capabilities, product and project management skills. The individual must be comfortable operating both strategically and tactically: establishing an AI enablement framework and roadmap for the department while also contributing directly to solution design, prompt development, testing, evaluation, and implementation.

Key Responsibilities

AI Solution Development and Lifecycle Management

Oversee the development of regional department-wide, sector/industry-specific, asset-class-specific, and team- or analyst-specific solutions across supported AI tools and platforms.

Responsibilities will span the complete solution lifecycle, including:

• Identify, solicit, collect, and document business problems, ideas, and AI use cases.

• Translate business needs into clear functional and technical requirements.

• Assess feasibility, value, risk, scalability, and appropriate implementation approaches.

• Design and develop AI-enabled solutions, workflows, prompts, and prototypes.

• Develop and refine prompts and prompting strategies for centrally developed and deployed AI products.

• Design and execute appropriate testing and evaluation methodologies.

• Validate solutions for quality, reliability, usability, and compliance with applicable requirements.

• Coordinate deployment and implementation.

• Provide or coordinate ongoing production support, maintenance, enhancement, and optimization.

• Comply with  appropriate lifecycle management for AI assets, including ownership, documentation, versioning, monitoring, review, and retirement where applicable.

• Promote reusable and scalable solutions where common needs exist, reducing unnecessary duplication of AI assets across Research teams.

AI Use-Case Intake, Prioritization and Roadmap

• Build and maintain strong relationships with Asia Pacific and Japan Research coverage teams to understand their workflows, priorities, challenges, and AI opportunities.

• Establish a sustainable process for soliciting, documenting, retaining, assessing, and prioritizing AI requirements and use cases surfaced by Research teams globally.

• Develop and maintain a transparent regional department-wide roadmap for AI enablement initiatives.

• Execute against the agreed roadmap and communicate priorities, dependencies, progress, and changes to stakeholders.

• Develop a consistent prioritization framework incorporating expected business value, user reach, implementation effort, technical feasibility, risk, strategic alignment, and potential for reuse.

Governance, Risk and Compliance

• Comply withlegal, compliance, risk, information-security, and AI governance processes required to introduce new AI assets and capabilities into Global Research.

• Partner with firmwide modeling, governance, and control teams to ensure compliance with applicable firmwide generative AI, model, data, and technology requirements.

• Ensure that required evaluations, approvals, controls, and documentation are completed and maintained.

• Ensure relevant requirements and restrictions are clearly communicated to Research users and stakeholders.

• Embed governance, control, data-handling, entitlements, and appropriate human-review considerations into the design of AI solutions from inception rather than treating governance as a post-development activity.

• Maintain appropriate documentation and auditability of AI enablement activities, decisions, evaluations, and approvals.

Technology, Data and Cross-Functional Collaboration

• Coordinate closely with firmwide technology teams responsible for AI platforms, infrastructure, and tool rollouts.

• Partner with technology development leads on the design and development of technology-built and supported AI assets.

• Work with technology teams to develop tools, utilities, software, and infrastructure required to support the AI Enablement team's activities.

• Collaborate with development teams, data teams, modeling teams, technology support teams, analytics/reporting teams, and other groups engaged in related AI initiatives.

• Coordinate with similar AI and enablement teams across Morgan Stanley's Institutional Technology organization to share knowledge, standards, solutions, and best practices.

• Act as a bridge between Research users and technical teams, translating investment-research requirements into actionable technology requirements and technical capabilities into practical Research applications.

• Identify opportunities to reuse firmwide technology and AI capabilities before initiating duplicative development.

Measurement, Analytics and Value Realization

• Collect, analyze, and report usage data for AI Enablement-supported tools, features, solutions, and initiatives.

• Monitor adoption patterns and identify opportunities to improve utilization and user experience.

• Use data and user feedback to inform roadmap priorities, training needs, solution enhancements, and tool-selection strategies

Required Skills and Qualifications

• At least 5  years of relevant professional experience, preferably within financial services, including investment banking, investment research, asset management, financial technology, financial-data providers/aggregators, or related organizations.

• Strong understanding of financial markets, financial analysis, and investment research workflows.

• Academic background in finance, economics, business, computer science, engineering, or a related discipline; demonstrated finance expertise is required.

• Strong hands-on Python capabilities.

• Minimum 2–3 years of practical experience with generative AI, large language models, prompt engineering, or related AI technologies.

• Demonstrated ability to design, develop, test, and improve prompts and AI-enabled workflows.

• Understanding of AI/LLM evaluation methodologies and the ability to translate evaluation concepts into practical testing frameworksz

• Demonstrated project and/or product management experience.

• Experience with data, data analytics, and usage/performance reporting.

• Ability to translate business requirements into functional and technical requirements. 

• Excellent written and verbal communication skills, including the ability to communicate technical AI concepts to non-technical audiences.

• Strong stakeholder-management and relationship-building capabilities.

• Ability to operate effectively and independently in the business environment. 

• Demonstrated ability to manage multiple priorities and initiatives in a complex, global organization.