Consultant Conversion- Senior Applied AI Engineer

Morgan Stanley•New York, NY

2d•$155,000 - $215,000

About The Position

The Senior Applied AI Engineer is a hands-on technical leadership role on Morgan Stanley’s Applied AI team within the Technology division. The team accelerates adoption of Generative AI by partnering with internal business and technology groups to identify, design, and validate high-value use cases. It provides engineering support, prototypes, reusable implementation patterns, technical guidance, and enablement rather than owning products. The engineer bridges Morgan Stanley’s firmwide AI platforms and application teams to help deliver secure, scalable, production-ready AI solutions. The role leads applied AI use cases from technical discovery and feasibility assessment through MVP definition, rapid prototyping, multi-agent development, evaluation, and handoff to the sponsoring application team. The engineer partners with stakeholders to frame problems, assess data and platform readiness, set measurable outcomes, make architecture and scope decisions, and build production-relevant prototypes that demonstrate value and establish a path to enterprise adoption.

Requirements

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 LLMs, prompt engineering, embeddings, vector databases, semantic search, 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, 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.

Nice To Haves

Experience with Azure AI Foundry, AWS Bedrock, Anthropic Claude, OpenAI GPT models, Google Gemini, or similar enterprise AI platforms and models.
Experience building and evaluating advanced RAG solutions, knowledge assistants, or AI-powered search capabilities.
Experience developing multi-agent systems and applying orchestration, delegation, memory, planning, and guardrail patterns.
Experience with LLM observability, tracing, evaluation platforms, synthetic test-data generation, or model and agent benchmarking.
Experience integrating applications with ServiceNow, Jira, Confluence, SharePoint, Microsoft Graph, Databricks, Snowflake, or similar enterprise platforms.
Familiarity with Kubernetes, containerized application deployment, cloud-native architectures, and production operational practices.
Experience working in financial services or another regulated industry.

Responsibilities

Lead technical discovery with business, product, architecture, data, security, and engineering stakeholders to clarify problems, workflows, constraints, data readiness, risks, and success criteria.
Assess the technical feasibility and business value of proposed Generative AI use cases, identify assumptions and dependencies, and recommend solution approaches.
Define outcome-focused MVPs, including scope, architecture, delivery plans, evaluation strategies, technical risks, and the boundary between prototype and production implementation.
Design and build proofs of concept, prototypes, and reference applications to validate assumptions, demonstrate value, and support stakeholder decisions.
Architect and develop AI-powered applications, copilots, assistants, and agentic workflows using Morgan Stanley 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 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, context management, and workflow orchestration patterns.
Build APIs, connectors, and integrations between AI solutions and enterprise systems, data platforms, and business workflows.
Establish evaluation frameworks and automated test suites for task completion, accuracy, groundedness, relevance, reliability, safety, latency, and cost, and use results to improve solutions.
Provide technical leadership through architecture reviews, design decisions, engineering guidance, code reviews, and hands-on problem solving.
Create reusable components, templates, implementation patterns, and developer examples for application teams.
Define transition criteria and support application teams moving validated prototypes toward production ownership through documentation, knowledge transfer, and implementation guidance.
Collaborate with firmwide AI platform teams and align solutions with enterprise engineering, security, governance, data protection, and Responsible AI standards.
Stay current with emerging AI technologies and assess and apply their practical relevance to business use cases.