Consultant Conversion- Senior Applied 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 Senior Applied AI Engineer 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.

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

The Applied AI team accelerates the adoption of Generative AI across Morgan Stanley. We partner with internal clients across business and technology organizations to identify, design, and validate high-value AI use cases that improve productivity, automate workflows, enhance decision-making, and unlock new business capabilities.

Applied AI serves as an engineering accelerator rather than a product development or ownership team. We provide hands-on development support, prototyping, reusable implementation patterns, technical guidance, and enablement to help application teams adopt and scale AI capabilities.

The team works closely with Morgan Stanley’s firmwide AI platform organizations, which provide foundational AI capabilities, models, infrastructure, governance frameworks, and shared services. Applied AI engineers bridge the gap between these foundational platforms and application development teams, helping internal clients accelerate delivery of secure, scalable, and production-ready AI solutions.

The Senior Applied AI Engineer will lead applied AI use cases from technical discovery and feasibility assessment through MVP scoping, rapid prototyping, multi-agent development, evaluation, and transition to the sponsoring application team.

This is a senior, hands-on engineering role combining technical leadership, solution architecture, and deep implementation expertise. The successful candidate will partner with business and technology stakeholders to frame problems, assess data and platform readiness, define measurable outcomes, scope pragmatic MVPs, and build production-relevant prototypes—including multi-agent solutions—that demonstrate value and establish a clear path to adoption on Morgan Stanley’s enterprise AI platforms.

The ideal candidate is an experienced software and AI engineer who can independently lead ambiguous use cases, make sound architecture and scope decisions, align cross-functional stakeholders, and remain hands-on across prototyping, agent development, integration, testing, and evaluation.

What you’ll do in the role:

• •Lead technical discovery with business, product, architecture, data, security, and engineering stakeholders to clarify the problem, user workflow, constraints, data readiness, risks, and success criteria.
• •Assess the technical feasibility and business value of proposed Generative AI use cases, identify key assumptions and dependencies, and recommend the appropriate solution approach.
• •Define outcome-focused MVPs, including scope, architecture, delivery plan, evaluation strategy, technical risks, and the boundary between prototype and production implementation.
• •Rapidly design and build proofs of concept, prototypes, and reference applications that validate critical assumptions, demonstrate measurable value, and accelerate stakeholder decisions.
• •Architect and develop AI-powered applications, copilots, assistants, and agentic workflows using Morgan Stanley’s enterprise AI platforms and services.
• •Design and implement multi-agent systems, including agent roles, orchestration, tool use, state and context management, inter-agent communication, human oversight, and failure handling.
• •Implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge repositories, vector databases, embeddings, semantic search, retrieval strategies, and grounding techniques.
• •Apply prompt engineering, structured outputs, tool calling, Model Context Protocol (MCP), context management, and workflow orchestration patterns to create reliable AI applications.
• •Build APIs, connectors, and integrations between AI solutions and enterprise systems, data platforms, and business workflows.
• •Establish evaluation frameworks and automated test suites to measure task completion, accuracy, groundedness, relevance, reliability, safety, latency, and cost; use results to drive iterative improvement.
• •Provide technical leadership through architecture reviews, design decisions, engineering guidance, code reviews, and hands-on problem solving across the use-case team.
• •Create reusable components, templates, implementation patterns, and developer examples that help application teams deliver AI solutions more efficiently.
• •Define transition criteria and support sponsoring application teams as validated prototypes move toward production ownership, including documentation, knowledge transfer, and implementation guidance.
• •Collaborate with firmwide AI platform teams and ensure solutions align with enterprise engineering, security, governance, data protection, and Responsible AI standards.
• •Stay current with emerging AI technologies, evaluate their practical relevance, and apply appropriate tools and techniques to business use cases.

What you’ll bring to the role:

• •7+ years of software engineering experience, including significant hands-on responsibility for designing and delivering enterprise applications.
• •3+ years of practical experience developing Generative AI applications, AI assistants, AI agents, copilots, or intelligent automation solutions.
• •Demonstrated experience leading a technical initiative or use case from discovery and scoping through prototype delivery, evaluation, and transition.
• •Advanced proficiency in Python and experience designing APIs, integrations, distributed services, and enterprise applications.
• •Strong knowledge of Large Language Models (LLMs), prompt engineering, embeddings, vector databases, semantic search, Retrieval-Augmented Generation (RAG), context management, and common AI application patterns.
• •Hands-on experience with one or more AI development and orchestration frameworks, such as LangGraph, LangChain, Semantic Kernel, Claude Agent SDK, OpenAI SDK, or similar technologies.
• •Experience designing and implementing AI agents, multi-agent workflows, tool integrations, orchestration, state management, and human-in-the-loop controls.
• •Familiarity with Model Context Protocol (MCP), AI gateways, agent-to-agent communication, tool-calling frameworks, and enterprise model access patterns.
• •Experience rapidly building proofs of concept, prototypes, or reference implementations while maintaining sound engineering practices and clear technical documentation.
• •Experience defining measurable success criteria and implementing automated evaluation for AI quality, groundedness, reliability, safety, performance, latency, and cost.
• •Experience integrating AI applications with enterprise systems, data platforms, APIs, identity controls, and business workflows.
• •Strong understanding of Responsible AI principles, AI governance, security controls, observability, and enterprise data protection requirements.
• •Experience with GitHub, CI/CD pipelines, containers, automated testing, code review, and modern software engineering practices.
• •Experience with Azure or AWS services used to build, host, and operate enterprise applications and AI workloads.
• •Ability to lead technical discussions, make and communicate trade-off decisions, and align business stakeholders, application teams, platform engineers, architects, and control partners.
• •Strong communication, collaboration, mentoring, and problem-solving skills, with the ability to operate effectively in ambiguous environments.

Preferred