Mistral AISingapore4h ago
Morgan StanleyPosted 1mo ago
Sr AI Engineer at Morgan Stanley scores 89 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI in this role
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 Engineering 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.
Partner with the Advanced Analytics, Machine learning and Gen AI Platform team(s), across multiple project areas, and work in collaboration with team(s) in India & US. The individual would be responsible for building autonomous systems that can reason, use tools, and complete multi-step tasks using LLMs/Reasoning models, build calibration scoring and guardrails for agent accuracy. The person would also be part of the overall cloud adoption and engineering roadmap and ensure scalable, agile and robust architecture and implementation. Additionally, should be able to work in a dynamic environment with limited or no supervision and should be able to knowledge share across other team members. Should be comfortable and manage time working with global team on multiple initiatives.
What you’ll do in the role:
Sr Hand-On Engineer who acts as the catalyst in building and deploying AI Agents at scale to accelerate technology and business roadmaps
Collaborate across multiple peer divisions to get alignment on AI Solutions demonstrate with POCs the “Art of the possible” and accelerate adoption
Evaluate state-of-art Gen AI centric technologies and prototype solutions to improve our architecture and platform
Design, Implement and Operationalize distributed, scalable, and reliable data flows that ingest, process, store, and access data at scale in batch / real-time used by AI Agents
What you’ll bring to the role:
Experienced professional with 6+ years of experience working towards building GenAI solutions & supporting components design, architecture, development, and operationalization of agent orchestrations at scale.
Agent Orchestration Frameworks: Mastery of frameworks like LangChain, LangGraph, CrewAI, and Microsoft's AutoGen to build multi-agent workflows.
LLM Implementation: Deep expertise in LLM APIs (OpenAI, Anthropic, AWS Bedrock), prompt engineering (Chain-of-Thought), and fine-tuning for specific agentic behaviors.
Memory & Context Management: Ability to implement complex memory systems, including vector databases (Pinecone, Weaviate) and Retrieval-Augmented Generation (RAG) pipelines.
Tool-Calling & APIs: Proficiency in integrating external APIs as agent "tools," including function calling and error handling for malformed model outputs.
Languages & Backend: Expert-level Python (mandatory), often paired with FastAPI, Node.js, or Go. Familiarity with Docker and Kubernetes for containerized deployment is standard.
Ability to work in Fast paced and Dynamic environment.
Good written and verbal communication skills
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.
Expected base pay rates for the role will be between $150,000 and $215,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.
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.
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
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Walk me through how you've used OpenAI in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Prompt Engineering, Rag, AI Agents, Fine Tuning, and OpenAI. 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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