Level

Morgan Stanley

AI Java Lead Engineer _ Vice President _Software Engineering

AI in this role

openaiclaudeanthropiccopilotlangchainlanggraphcrewaiautogenazure-openaiclaude-code
prompt-engineeringragllm-integration

AI Java Lead Engineer _ Vice President _Software Engineering 


We're seeking someone to join our Institutional Securities Technology (Prime Brokerage AND Institutional Equity) team as a Java Backend Engineer to build scalable, high-performance enterprise applications on a team that's adopting agentic AI workflows (built on frameworks like the Claude Agent SDK). This is fundamentally a backend engineering role — the bar is deep Java/Spring Boot craftsmanship and strong problem-solving. AI/agentic experience is crucial; we need someone who is aware & can quickly pick up orchestrator/subagent patterns & eval workflows.


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 Lead Software Engineer position at Vice President Level, which is part of the job family responsible for developing and maintains software solutions that support business needs. 


Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.


What you’ll do in the role:

  • Hands-on Java development.
  • Deep Spring Boot, microservices, REST APIs, messaging integrations (Kafka, JMS, RabbitMQ).
  • Strong SQL, concurrency & distributed systems design
    CI/CD, DevOps, production observability.
  • Experience in performance tuning and scalability improvements.
  • Demonstrated strong problem-solving ability and comfort learning new technologies quickly

What you’ll bring to the role:

  • Genuine interest in AI-assisted / agentic software development, with basic hands-on exposure (personal projects, POCs, or production) to LLM-integrated systems — e.g., calling OpenAI/Azure OpenAI/Anthropic APIs, or using tools like GitHub Copilot / Claude Code in a substantial way.
  • Ability to grasp and reason about agentic concepts: orchestrator/subagent patterns, tool/function calling, eval-driven iteration ("hill climbing"), and basic prompt engineering.
  • Python exposure for AI integration, scripting, or data pipeline use cases.
  • Hands-on experience integrating LLMs into production backend systems (OpenAI, Azure OpenAI, Anthropic, or equivalent).
  • Working knowledge of agentic AI frameworks — LangChain, LangGraph, Spring AI, AutoGen, CrewAI, or Claude Agent SDK — including agent memory, tool calling, and multi-step reasoning loops.
  • Ability to design multi-agent workflows: orchestrator/subagent patterns, agent-to-agent communication, and guardrails for autonomous systems.
  • Awareness of AI system reliability concerns: hallucination mitigation, observability (tracing agent runs), latency, and cost management.
  • Curiosity and self-driven experimentation with emerging agentic patterns (ReAct, Chain-of-Thought, reflection loops, human-in-the-loop gates).
  • RAG architecture and Vector Database concepts.
  • Financial Services or Investment Banking domain experience.
  • At least 6 years' relevant experience would generally be expected to find the skills required for this role.

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:

At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years.  Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.

To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices​ into your browser.

Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background.  Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.

Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.

For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.

How we rate this

AI Java Lead Engineer _ Vice President _Software Engineering at Morgan Stanley rates 84 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRAGLLM IntegrationOpenAIClaudeAnthropicCopilotLangChain

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. How have you integrated a large language model into a production application?
  4. What's a project where you used OpenAI hands-on?
  5. Walk me through how you've used Claude in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Prompt Engineering, RAG, LLM Integration, OpenAI, and Claude. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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