Risk-Dallas-Vice President-Software Engineering

Goldman Sachs•Dallas, TX

1d•Onsite

About The Position

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.

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

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.