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. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering, which is comprised of our Technology Division and global strategists' groups, 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.

Who We Look For

Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.

Asset and Wealth Management (AWM)

Bringing together traditional and alternative investments, we provide clients around the world with a dedicated partnership and focus on long-term performance. As the firm's primary investment area, we provide investment and advisory services for some of the world's leading pension plans, sovereign wealth funds, insurance companies, endowments, foundations, financial advisors and individuals, for which we oversee more than $2 trillion in assets under supervision. Working in a culture that values integrity and transparency, you will be part of a diverse team that is passionate about our craft, our clients, and building sustainable success.

What You'll Do

The Asset Management Engineering team is seeking an Associate Software Engineer with an AI Engineering focus, based in Dallas. In this role, you will design and build AI-powered tools that transform how investment professionals analyze deals, manage portfolios, and make decisions. You will work at the intersection of software engineering and applied AI, turning large language models and emerging AI capabilities into production-grade applications for the alternatives business.

• Build and maintain AI-powered applications that automate and enhance investment workflows
• Design and develop APIs, data pipelines, and backend services that integrate AI models into existing platforms
• Implement retrieval-augmented generation (RAG) systems, prompt engineering frameworks, and agentic AI architectures
• Collaborate closely with investment professionals, data scientists, and product stakeholders to translate business needs into technical solutions
• Ensure reliability, scalability, and security of AI-driven applications in a production environment
• Stay current with the rapidly evolving AI landscape and evaluate new tools, frameworks, and techniques for practical application

Basic Qualifications

• 2+ years of software engineering experience
• Proficiency in Python and at least one additional programming language (e.g., Java, TypeScript)
• Experience building and deploying applications that leverage large language models or generative AI
• Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Claude)
• Experience with retrieval-augmented generation (RAG) and vector stores
• Familiarity with RESTful APIs, cloud services, and modern software development practices
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment
• Effective written and verbal communication skills

Preferred Qualifications

• Experience with AI orchestration frameworks (e.g., LangChain, LlamaIndex, or similar)
• Familiarity with Graph RAG and knowledge graphs for complex data relationships
• Experience with prompt engineering and optimization techniques
• Exposure to cloud platforms (AWS, GCP, or Azure)
• Experience with containerization tools such as Docker and Kubernetes
• Background in financial services or fintech
• Ability to work across globally distributed teams and engage with non-technical stakeholders