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Role: Vice President - AI Engineer
Division: Risk Engineering - Market Risk
Location: Dallas, Americas 

About Goldman Sachs

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 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 the firm's strategic plan. Operating in a fast paced and dynamic environment and utilizing the best in class risk tools and frameworks, Risk teams are analytically curious, have an aptitude to challenge, and an unwavering commitment to excellence. To ensure uncompromising accuracy and timeliness in the delivery of the risk metrics, our platform is continuously growing and evolving. Risk Engineering combines the principles of Computer Science, Mathematics and Finance to produce large scale, computationally intensive calculations of risk Goldman Sachs faces with each transaction we engage in.

Role Overview - Market Risk AI Engineering 

We are seeking an Engineer with 9+ years of experience to join the Market Risk Platform team. You will work with a team of talented engineers to drive the build & adoption of common tools, platforms, and applications. The team builds solutions that are offered as a software product or as a hosted service. We are a dynamic team of talented developers and architects who partner with business areas and other technology teams to deliver high profile projects using a raft of technologies that are fit for purpose (Java, Cloud computing, HDFS, Spark, S3, ReactJS, Sybase IQ among many others). A glimpse of the interesting problems that we engineer solutions for, include acquiring high quality data, storing it, performing risk computations in limited amount of time using distributed computing, and making data available to enable actionable risk insights through analytical and response user interfaces.

Key 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, earlywarning indicators, and forecasting.
• Build endtoend 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 AIdriven solutions.
• Optimize Agents' performance, scalability, and reliability in distributed and cloudbased environments.
• Mentor junior engineers and contribute to code reviews, design discussions, and architecture decisions.

Skills & Experience Required Qualifications

• 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.
• Handson 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

What We Offer

• Opportunity to work at the intersection of AI, engineering, and market risk at a global scale.
• Highimpact 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.