Goldman Sachs , we commit our people, capital, and ideas to help our clients, shareholders, and the communities we serve to grow. Founded in 1869, Goldman Sachs is a leading global investment banking, securities, and investment management firm. Headquartered in New York, we maintain offices around the world.
The
Corporate Treasury
division is responsible for measuring, monitoring, and managing the firm's liquidity position under both normal and stressed conditions. As liquidity markets, regulatory expectations, and data complexity continue to evolve, advanced analytics and artificial intelligence are becoming central to how liquidity risk is assessed and managed. Our teams operate in a fast-paced, dynamic environment and are analytically curious, technically strong, and deeply engaged with the firm's evolving risk profile.
Role Overview - Liquidity Risk AI Engineering
We are seeking an
AI Engineer with 5+ years of experience
to join the Liquidity Risk technology team. In this role, you will design, build, and deploy AI-driven solutions that enhance liquidity risk monitoring, stress testing, scenario generation, and decision support. You will work closely with liquidity risk managers, quantitative teams, and engineering partners to translate complex risk problems into scalable, production-ready AI systems.
Key Responsibilities
Design, develop, and deploy
machine learning and AI models
to support liquidity risk metrics, stress scenarios, early‑warning indicators, and forecasting.
Build
end‑to‑end AI pipelines , including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
Apply
supervised, unsupervised, and time‑series modeling techniques
to large‑scale financial and transactional datasets.
Partner with liquidity risk managers and quantitative teams to
translate regulatory and business requirements into AI-driven solutions .
Optimize agents' performance, scalability, and reliability in
distributed and cloud‑based environments .
Contribute to the firm's
AI engineering standards , including testing, model documentation, and production controls.
Mentor junior engineers and contribute to code reviews, design discussions, and architecture decisions.
Skills & Experience Required Qualifications
5+ years of professional experience
as an AI Engineer in a production environment.
Hands‑on experience in integrating LLM models using agents and developing monitoring and observability tools for those agents.
Experience with AWS BedRock platform, especially using AWS Agent Core for deploying agents.
Experience in developing agents using Google AdK or LangGraph frameworks and deploying them on AWS.
Exposure to distributed computing frameworks and workflow orchestration tools (e.g., Airflow).
Strong proficiency in
Python
and experience with ML/AI libraries such as
PyTorch , or similar.
Solid understanding of
machine learning fundamentals , including model selection, bias‑variance tradeoffs, and evaluation techniques.
Experience working with
large, structured datasets
using SQL and distributed data platforms (cloud data warehouses).
What We Offer
Opportunity to work at the intersection of
AI, engineering, and liquidity risk
at a global scale.
High‑impact role influencing how the firm measures and manages liquidity under stress.
Collaborative environment with exposure to senior risk managers, quants, and technology leaders.
Ongoing learning, development, and career progression within the Liquidity and Engineering organizations.