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 forward-thinking Cloud & AI Agent Specialist to join our Data Engineering team. This role is at the cutting edge of the "Agentic Shift," where you will move beyond static LLM implementations to build autonomous, goal-oriented systems. You will be responsible for designing and deploying AI Agents that can reason, use tools, and interact with complex data environments.

A primary focus of this role is the development and management of MCP (Model Context Protocol) Servers. You will act as the architect of the "connective tissue" between our Large Language Models and our enterprise data stack, ensuring that agents have secure, real-time access to the context they need to perform high-value tasks.

Key Responsibilities

• Agentic Framework Development: Design and implement multi-agent systems using frameworks such as LangChain, CrewAI, or AutoGPT. Develop custom logic for task decomposition, tool selection, and error recovery.
• MCP Server Engineering: Build, deploy, and maintain MCP Servers (using Python or TypeScript) to expose databases, APIs, and local file systems to AI agents. Standardize how models discover and invoke enterprise tools.
• Cloud Infrastructure: Deploy agentic workloads on AWS, Azure, or GCP using containerization (Docker, Kubernetes) and serverless architectures (AWS Lambda, Azure Functions).
• Data Science & Modeling: Apply data science principles to optimize agent performance. This includes fine-tuning models (e.g., via LoRA), implementing RAG (Retrieval-Augmented Generation) pipelines, and managing vector databases (e.g., Pinecone, Weaviate).
• Context & Prompt Engineering: Develop sophisticated prompt templates and context-injection strategies to minimize hallucinations and ensure agents adhere to complex business logic.
• Agentic Observability: Implement monitoring and evaluation frameworks (e.g., LangSmith, Arize Phoenix) to track agent trajectories, tool-call accuracy, and cost-to-output ratios.
• Security & Governance: Ensure all agents and MCP servers operate under "least-privilege" access. Implement guardrails to prevent prompt injection and unauthorized data egress.
• Collaboration: Work closely with Data Engineers to ensure data quality for agent consumption and with Product teams to translate business requirements into agentic workflows.

Basic Qualifications

• Experience: 2–7 years in a technical role such as AI Engineer, Data Scientist, or Backend Developer with a heavy focus on LLM integration.
• Agentic Expertise: Proven experience building agents that use tools (Function Calling) to complete multi-step tasks.
• MCP Proficiency: Hands-on experience developing MCP Servers and understanding the client-server communication model of the protocol.
• Programming: Advanced proficiency in Python (Asyncio, Pydantic) and/or TypeScript/Node.js.
• Cloud & DevOps: Experience with cloud-native deployment, CI/CD for AI (LLMOps), and Infrastructure as Code (Terraform).
• Data Stack: Familiarity with SQL/NoSQL databases and the integration of semantic layers into AI workflows.
• Problem Solving: Ability to debug non-deterministic systems where "code" (LLM output) may vary between executions.

Preferred Qualifications

• Contributions to open-source agentic frameworks or MCP server repositories.
• Experience with Small Language Models (SLMs) for edge-based agentic tasks.
• Certifications in Cloud AI (e.g., AWS Certified AI Practitioner, Azure AI Engineer Associate).

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.