Risk-Dallas-Vice President-Software Engineering

About the role

The Vice President – AI Engineer position is located in the Risk Engineering – Market Risk division at Goldman Sachs in Dallas, Americas. Goldman Sachs is a leading global investment banking, securities, and investment management firm headquartered in New York with offices worldwide. The Risk Business identifies, monitors, evaluates, and manages the firm's financial and non-financial risks in support of the firm's Risk Appetite Statement and strategic plan. This role involves building and adopting common tools, platforms, and applications for the Market Risk Platform team.

Responsibilities

• Build internal and external reporting for the output of risk metric calculation using data extraction tools, such as SQL, and data visualization tools, such as Tableau.
• Utilize web development technologies to facilitate application development for front end UI used for risk management actions.
• Develop software for calculations using databases like Snowflake, Sybase IQ and distributed HDFS systems.
• Design and support batch processes using scheduling infrastructure for calculation and distributing data to other systems.
• Design, develop, and deploy machine learning and AI models to support market 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.
• Partner with 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.
• Mentor junior engineers and contribute to code reviews, design discussions, and architecture decisions.

Requirements

• 9+ years of professional experience as an Engineer in a production environment.
• Exposure to distributed computing frameworks and workflow orchestration tools (e.g., Airflow).
• Experience working with large, structured datasets using SQL and distributed data platforms (cloud data warehouses).
• Strong proficiency in Python and experience with ML/AI libraries such as PyTorch, or similar.
• Hands-on experience in integrating LLM models using agents and developing monitoring and observability tools for those agents is a plus.
• Experience in developing agents using Google ADK or Lang Graph frameworks and deploying them on AWS is a plus.

Skills

• Python proficiency.
• Experience with ML/AI libraries such as PyTorch.
• Experience with distributed computing frameworks and workflow orchestration tools.
• Experience with large, structured datasets using SQL and distributed data platforms.
• Experience with integrating LLM models using agents and developing monitoring and observability tools.
• Experience with developing agents using Google ADK or Lang Graph frameworks and deploying them on AWS.

Benefits

High-impact role influencing how the firm measures and manages market risk 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.

Pay

N/A

Schedule

N/A