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Job Description 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, 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. 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. 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 Exposure to AWS services like S3, ECS, MWAA, Lambda, Dynamo DB is a plus What We Offer Opportunity to work at the intersection of AI, engineering, and market risk at a global scale. 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.