Job Description

Who We Are

At Goldman Sachs, we connect people, capital and ideas to help solve problems for our clients. We are a leading global financial services firm providing investment banking, securities and investment management services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals.

In Private Wealth Management, we help our clients pursue their wealth management goals through careful advice & astute investment management. PWM Engineering plays a pivotal role in building the tools and applications our business needs to effectively manage and support our client’s diverse requirements. We support the entire user experience starting with onboarding through to trading, reporting, as well as providing clients access to their portfolios online via native iOS and Android apps. We also build and support numerous applications for our Business and Operations users to help them effectively manage risk and provide the best white-glove service possible to our clients.

The Data Distribution team supports PWM's Quantum data distribution platform which is considered the primary source of data relating to Client Holdings, Transactions, Taxlots and reference data like Accounts, Products and Prices in PWM. Several teams/projects requires this data and our team has built several distribution channels for different usage patterns.

We are currently enhancing our platform based on new business requirements as well as increasing stability and scalability needs to support a growing business. The platform will be required to manage very high volumes of requests with varying time sensitivities and prioritising across multiple tenants via an event based framework.

Your Impact

We are seeking a motivated and technically curious Junior Cloud & AI Agent Analyst (1–3 years of experience) to join our Data Engineering team. In this role, you will support the development and operationalization of autonomous AI agents and the infrastructure that connects them to our enterprise data. You will work at the intersection of Generative AI and Cloud Computing, helping to build systems that don't just "chat," but actually execute tasks.

Key Responsibilities

• Agent Implementation: Assist in building and deploying AI agents using frameworks such as LangChain, CrewAI, or LangGraph. You will help configure agent roles, goals, and "backstories" to ensure reliable task execution.
• Cloud Deployment: Support the deployment of agentic and other workloads on AWS, Azure, or GCP. This includes working with containerized environments (Docker) and serverless functions to host agent logic and MCP integrations.
• Prompt & Tool Engineering: Craft and refine prompt templates and tool definitions. You will work to ensure that agents understand when and how to call specific tools via MCP to retrieve real-time data.
• Testing & Evaluation: Participate in the "LLMOps" lifecycle by testing agent trajectories. You will use tools like LangSmith or Arize Phoenix to identify where agents fail, hallucinate, or enter infinite loops, and suggest fixes.
• Data Integration: Assist in managing Vector Databases (e.g., Pinecone, Weaviate) and RAG (Retrieval-Augmented Generation) pipelines that provide agents with long-term memory and domain-specific knowledge.
• Security & Monitoring: Monitor agent activity for "runaway" costs or unauthorized tool usage. You will help implement basic guardrails and "human-in-the-loop" checkpoints for sensitive agent actions.
• Documentation: Maintain clear documentation for MCP server schemas and agent workflows to ensure the team can scale and troubleshoot the agentic ecosystem effectively.

Basic Qualifications

• Experience: 1–3 years in a technical role such as Junior Developer, Data Analyst, or Cloud Support Engineer.
• Programming Foundations: Proficiency in Python (preferred) or Java Familiarity with asynchronous programming is a plus.
• AI Interest: A foundational understanding of LLMs, prompt engineering, and the concept of "tool calling" or "function calling."
• Cloud Basics: Basic experience with at least one major cloud provider (AWS, Azure, or GCP) and an understanding of how to deploy simple web services or APIs.
• API Knowledge: Comfortable working with REST APIs and JSON data structures—essential for building MCP servers.
• Problem Solving: A methodical approach to debugging non-deterministic systems and a high degree of adaptability in a rapidly changing tech landscape.

Preferred Qualifications

• Familiarity with the Model Context Protocol (MCP) specification.
• Experience with version control ( Git) and basic CI/CD concepts.
• Academic or project-based experience with Vector Stores or semantic search.
• Relevant certifications (e.g., AWS Cloud Practitioner, Microsoft AI Fundamentals).

Goldman Sachs Engineering Culture

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here!

© The Goldman Sachs Group, Inc., 2026 All rights reserved.
Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity.