Job Title: Applied AI Engineer
Position Summary
The Applied AI Engineer will work with business and technology teams to develop and implement Generative AI solutions. This is a hands-on engineering role focused on building prototypes, implementing AI capabilities, integrating enterprise systems, and creating reusable components that accelerate delivery. The successful candidate will work with internal clients to understand use cases, develop practical solutions, and support application teams as they move AI capabilities toward production using enterprise AI platforms and services. The ideal candidate is a capable software engineer with practical experience building Generative AI applications, a willingness to learn emerging technologies, and the ability to collaborate effectively with business and technology partners.
Key Responsibilities
- Work with business and technology stakeholders to understand Generative AI use cases and translate requirements into practical implementations.
- Develop proofs of concept, prototypes, and reference applications that demonstrate business value and accelerate AI adoption.
- Build and enhance AI-powered applications, copilots, assistants, and agentic workflows using Morgan Stanley’s enterprise AI platforms and services.
- Implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge repositories, vector databases, embeddings, semantic search, and retrieval techniques.
- Develop AI agents and orchestration workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, Claude Code SDK, OpenAI SDK, or similar technologies.
- Implement prompt engineering techniques, structured outputs, tool calling, context management, and workflow orchestration patterns.
- Build APIs, connectors, and integrations between AI applications and enterprise systems, data platforms, and business workflows.
- Create reusable components, templates, implementation patterns, and developer examples that help application teams deliver AI solutions more efficiently.
- Develop automated tests and evaluation capabilities to measure solution quality, grounding, reliability, safety, and performance.
- Troubleshoot implementation issues and support application teams as prototypes and reference solutions transition toward production.
- Collaborate with firmwide AI platform teams to use foundational AI capabilities and follow enterprise engineering, security, governance, and Responsible AI standards.
- Stay current with emerging AI technologies and apply relevant tools and techniques to business use cases.
Required Qualifications
- 3 to 5 years of software engineering experience with hands-on application development responsibilities.
- 1 to 3 years of practical experience developing Generative AI applications, AI assistants, AI agents, copilots, or intelligent automation solutions.
- Proficiency in Python and experience building APIs, integrations, and enterprise applications.
- Working knowledge of Large Language Models (LLMs), prompt engineering, embeddings, vector databases, semantic search, Retrieval-Augmented Generation (RAG), and common AI application patterns.
- Hands-on experience with one or more AI development frameworks, such as LangGraph, LangChain, Claude Code SDK, OpenAI SDK, or similar technologies.
- Familiarity with Model Context Protocol (MCP), AI gateways, agent-to-agent communication, or tool-calling frameworks.
- Experience implementing AI agents, orchestration workflows, tool integrations, or agentic applications.
- Experience building proofs of concept, prototypes, or reference implementations.
- Experience integrating applications with enterprise systems, data platforms, and APIs.
- Familiarity with automated testing and evaluation approaches for AI solution quality, safety, reliability, and performance.
- Awareness of Responsible AI principles, AI governance, security controls, and enterprise data protection requirements.
- Experience with GitHub, CI/CD pipelines, containers, and modern software engineering practices.
- Familiarity with Azure or AWS services used to host and operate applications and AI workloads.
- Ability to work with business stakeholders and application development teams to translate requirements into working software.
- Strong communication, collaboration, and problem-solving skills.
Preferred Qualifications
- Experience with Azure AI Foundry, AWS Bedrock, Anthropic Claude, OpenAI GPT models, SpaceX Grok Models, Gemini, or similar enterprise AI platforms.
- Experience building RAG solutions, knowledge assistants, or AI-powered search capabilities.
- Exposure to multi-agent systems and agent orchestration workflows.
- Experience integrating applications with ServiceNow, Jira, Confluence, SharePoint, Microsoft Graph, Databricks, Snowflake, or similar enterprise platforms.
- Familiarity with Kubernetes, containerized application deployment, and cloud-native architectures.
- Experience working in financial services or another regulated industry.
