Software Engineer, Global Investment Research, Agentic Systems

Goldman Sachs (lateral); Dallas (TX); On-site; USD 150,000 - 230,000; Full time

Job summary

Goldman Sachs is seeking an Agentic Systems Engineer to build end-to-end AI-enabled research tools and agentic pipelines. You will collaborate with senior engineers to deliver full-stack features, integrating frontends with backend services in Java or Python.

You will implement and optimize RAG pipelines, tool calls, and human-in-the-loop steps using Google ADK, LangChain, LangGraph, AWS Bedrock, and related technologies, while ensuring reliability, security, and scalability.

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, plus 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.

Responsibilities

• Feature Engineering: Build and maintain end-to-end full-stack features, integrating analyst-facing frontends with backend APIs and microservices 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.
• Evaluation & Benchmarking: Develop automated offline and online evaluation suites to assess contextual relevance, groundedness, and hallucination reduction.
• Cloud & Data Integration: Deploy and connect AI components with enterprise services on AWS via Lambda, ECS/Fargate, S3, IAM, and API Gateway endpoints.
• Observability & Incident Support: Monitor performance with CloudWatch and tracing; triage incidents and optimize token/resource usage.
• SDLC Discipline: Participate in peer reviews, write tests, maintain CI/CD pipelines, document integration patterns and data models.
• Agile Collaboration: Work with stakeholders in Agile sprints to translate research requirements into technical stories.

Skills

Java

Python

Full-stack web

React

TypeScript

CI/CD

Testing

Education

Bachelor's degree in CS/SE/Applied Math

Tools

Google ADK

LangChain

LangGraph

AWS Bedrock

S3

Lambda

ECS/Fargate

API Gateway

Job description

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.

Benefits

Goldman Sachs is committed to providing our people with valuable and competitive benefits and wellness offerings, as it is a core part of providing a strong overall employee experience. A summary of these offerings, which are generally available to active, non-temporary, full-time and part-time US employees who work at least 20 hours per week, can be found here.