Consultant Conversion- Senior Applied AI Engineer
Morgan Stanley2 days ago
Base Salary
$155k - $215k/yr
Responsibilities
- Lead technical discovery with business, product, architecture, data, security, and engineering stakeholders.
- Assess generative AI feasibility, business value, data readiness, risks, dependencies, and solution approaches.
- Define outcome-focused MVP scope, architecture, delivery plans, evaluation strategies, and technical risks.
- Build proofs of concept, prototypes, reference applications, AI-powered applications, copilots, assistants, and agentic workflows.
- Design multi-agent systems with orchestration, tool use, state and context management, inter-agent communication, human oversight, and failure handling.
- Implement RAG solutions using enterprise knowledge repositories, vector databases, embeddings, semantic search, retrieval strategies, and grounding techniques.
- Apply prompt engineering, structured outputs, tool calling, MCP, context management, and workflow orchestration patterns.
- Build APIs, connectors, and integrations with enterprise systems, data platforms, and business workflows.
- Create automated evaluation and testing frameworks covering accuracy, groundedness, relevance, reliability, safety, latency, and cost.
- Provide architecture reviews, design decisions, code reviews, engineering guidance, mentoring, and hands-on problem solving.
- Develop reusable components, templates, implementation patterns, and developer examples for application teams.
- Support transition of validated prototypes to sponsoring application teams through documentation, knowledge transfer, and implementation guidance.
- Collaborate with AI platform teams and align solutions with engineering, security, governance, data protection, and Responsible AI standards.
Requirements
- At least 7 years of software engineering experience, including significant hands-on enterprise application design and delivery.
- At least 3 years of practical experience developing generative AI applications, assistants, agents, copilots, or intelligent automation solutions.
- Experience leading a technical initiative or use case from discovery and scoping through prototype delivery, evaluation, and transition.
- Advanced Python proficiency 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 AI application patterns.
- Hands-on experience with AI development or orchestration frameworks such as LangGraph, LangChain, Semantic Kernel, Claude Agent SDK, or OpenAI SDK.
- Experience designing AI agents, multi-agent workflows, tool integrations, orchestration, state management, and human-in-the-loop controls.
- Familiarity with 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 with sound engineering practices and documentation.
- Experience defining measurable success criteria and implementing automated evaluation for AI quality, reliability, safety, performance, latency, and cost.
- Experience integrating AI applications with enterprise systems, data platforms, APIs, identity controls, and business workflows.
- Understanding of Responsible AI, AI governance, security controls, observability, and enterprise data protection requirements.
- Experience with GitHub, containers, automated testing, code review, and modern software engineering practices.
- Experience with Azure or AWS services for enterprise applications and AI workloads.
- Ability to lead technical discussions, make trade-off decisions, align stakeholders, communicate effectively, collaborate, mentor, and solve problems in ambiguous environments.
- Preferred experience with Azure AI Foundry, AWS Bedrock, Anthropic Claude, OpenAI GPT models, Google Gemini, advanced RAG, knowledge assistants, AI search, multi-agent systems, LLM observability, tracing, evaluation platforms, synthetic test-data generation, benchmarking, ServiceNow, Jira, Confluence, SharePoint, Microsoft Graph, Databricks, Snowflake, Kubernetes, containerized deployment, cloud-native architectures, production operations, or regulated industries.
Benefits
- Morgan Stanley offers comprehensive employee benefits and perks, support for employees and families, diversity and inclusion programs, and opportunities for internal career mobility.
- The role offers expected base pay of $155,000 to $215,000 per year, with additional compensation and benefits potentially available.
Tech Stack
Categories
About Morgan Stanley
Morgan Stanley is a global financial services firm that provides investment banking, sales and trading, wealth management, and investment management to corporations, governments, institutions, and individuals. It earns fees, commissions, and interest and trading income across advisory, underwriting, brokerage, and asset management businesses. Founded in 1935 and headquartered in New York, the public company operates in more than 40 countries through thousands of offices, including a large U.S. wealth management franchise.