Corporate Treasury, Liquidity Risk, AI Engineer, Vice President, Dallas

Goldman Sachs•Dallas, TX

1d•Onsite

About The Position

At 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.

Requirements

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 Bed Rock platform especially using AWS Agent core for deploying agents
Experience in developing agents using Google ADK or Lang Graph 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 trade‑offs, and evaluation techniques.
Experience working with large, structured datasets using SQL and distributed data platforms (cloud data warehouses)

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.