Build and productionize AI-powered research tools and agentic pipelines for Global Investment Research. Develop full-stack features with Java or Python, implement RAG and multi-agent workflows, integrate AWS services, create evaluation and benchmarking systems, monitor production performance, support incident resolution, and maintain testing, CI/CD, documentation, and Agile delivery practices.
What We Do
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. Drive new businesses, redefine finance using AI, and seize opportunities at market speed.
In an era defined by AI, Engineering isn't just central – it's the driving force behind our business. Our dynamic environment demands innovative strategic thinking and immediate, impactful solutions. Ready to push the limits of digital possibility? Begin your journey here.
Who We Look For
Goldman Sachs Global Investment Research (GIR) is seeking an Agentic Systems Engineer to build, enhance, and productionize intelligent research tools and automated agentic pipelines. You will collaborate with senior engineers to develop end-to-end full-stack applications, implement advanced RAG components, and integrate AI services into mission-critical GIR platforms.
How You Will Fulfill Your Potential
- Feature Engineering: Build and maintain end-to-end full-stack features, integrating analyst-facing frontends with backend APIs and microservices implemented in Java or Python.
- Gen AI & Agent Implementation: Implement and optimize advanced RAG pipelines and stateful multi-agent workflows using Google ADK, LangChain, LangGraph, AWS Bedrock, or equivalent technologies, incorporating structured outputs, tool calling, and human-in-the-loop steps.
- Evaluation & Benchmarking: Implement automated offline and online evaluation suites using tools such as Braintrust, AgentCore Eval, equivalent industry solutions, or custom test harnesses to assess contextual relevance, groundedness, and hallucination reduction.
- Cloud & Data Integration: Deploy and connect AI components with enterprise services on AWS, configuring Lambda, ECS/Fargate, EventBridge, S3, IAM policies, and API Gateway endpoints.
- Observability & Incident Support: Monitor model and service performance using CloudWatch and distributed tracing; triage production incidents, conduct root-cause analyses, and optimize token/resource consumption.
- SDLC Discipline: Actively participate in peer code reviews, write automated unit and integration tests, maintain CI/CD pipelines, and document integration patterns and data models.
- Agile Collaboration: Coordinate with business stakeholders and cross-functional teams in Agile sprints to translate research requirements into deliverable technical stories.
Basic Qualifications
- Bachelor’s degree in Computer Science, Software Engineering, Applied Mathematics, or a related quantitative field.
- 3–5 years of hands-on software engineering experience with proficiency in Java or Python, combined with modern full-stack web technologies (e.g., React, TypeScript, FastAPI, Spring Boot).
- Practical experience building Gen AI applications using Google ADK, LangGraph, or equivalent agentic frameworks, vector databases, and AWS Bedrock.
- Experience implementing evaluation pipelines and prompt testing with Braintrust, AgentCore Eval, equivalent technologies, or custom benchmark harnesses.
- Working knowledge of core AWS services, including Lambda, ECS/Fargate, S3, EventBridge, CloudWatch, and API Gateway.
- Solid understanding of standard SDLC tooling, automated testing, Git, Docker, and CI/CD automation.
Preferred Qualifications
- Exposure to financial domain data, investment research workflows, or enterprise content authoring tools.
- Practical understanding of token optimization, prompt caching, and latency reduction strategies.
- Familiarity with guardrail frameworks defending against prompt injection and unauthorized data access.
- Experience building asynchronous or event-driven microservices across polyglot Java and Python stacks.
Please note we will only respond to applications for which we have an interest.
Goldman Sachs New York, New York, USA Office
200 West Street, New York, NY, United States, 10282
Goldman Sachs Edison, New Jersey, USA Office
Edison, United States
Goldman Sachs Jersey City, New Jersey, USA Office
Jersey City, United States
Goldman Sachs New York, New York, USA Office
New York, United States
Goldman Sachs Newark, New Jersey, USA Office
Newark, United States
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