Vice President, Market Risk, Cloud, Software Engineering

Goldman Sachs

Quick summary

Work type: On-site
Location: Dallas, TX
Posted: 1 day ago
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $182k

$134k most similar roles pay here $222k

This listing doesn't post a salary. Most similar roles pay $157,200–$206,100.

Based on 240 similar postings.

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About Goldman Sachs

Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.

Goldman Sachs currently has 187 open roles on FindRole.

Listed pay typically runs $130,000–$250,000 across 60 roles with salary data.

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At a glance

TL;DR · Vice President, Market Risk, Cloud, Software Engineering

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As Vice President of AI Engineering at Goldman Sachs in Dallas, you will join the Risk Engineering Market Risk team as a seasoned engineer with over nine years of experience to drive the build and adoption of common tools, platforms, and applications. Your daily responsibilities include designing end-to-end AI pipelines for market risk metrics, developing software using Snowflake, Sybase IQ, and HDFS systems, and integrating machine learning models in distributed cloud environments. You will work closely with risk managers and quantitative teams to translate regulatory requirements into actionable solutions, optimize agents' performance, and mentor junior engineers. The role requires expertise in Python, ML/AI libraries like PyTorch, and experience with large datasets using SQL and distributed data platforms. This high-impact position offers the opportunity to influence market risk management at a global scale within a collaborative environment.

Skills

Python PyTorch AWS Airflow SQL Snowflake Sybase IQ HDFS Spark S3 ReactJS Tableau Docker CI/CD MLOps LLM models Google ADK Lang Graph

What you'll do

Design and deploy machine learning models for market risk metrics and forecasting.
Build end-to-end AI pipelines from data ingestion to model deployment and monitoring.
Develop software for risk computations using distributed HDFS systems and databases.
Utilize web development technologies to create front-end UIs for risk management actions.
Optimize performance, scalability, and reliability of agents in cloud-based environments.

What we're looking for

9+ years of professional engineering experience in a production environment.
Expertise in Python and ML/AI libraries like PyTorch or similar.
Experience with large structured datasets using SQL and distributed data platforms.
Hands-on development of end-to-end AI pipelines including model deployment and monitoring.
Design and implementation of machine learning models for market risk metrics and forecasting.
Proficiency in distributed computing frameworks and workflow orchestration tools.

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