Corporate Treasury-Bengaluru-Analyst-Software Engineering

Corporate Treasury-Bengaluru-Analyst-Software EngineeringBengaluru, Karnataka, India

AI/ML Engineer – Associate– Corporate Treasury Engineering

Who We Are

Corporate Treasury lies at the heart of Goldman Sachs, ensuring all the businesses have the appropriate level of funding to conduct their activities, while also optimizing the firm’s liquidity, managing its risk and compliance with regulations.

Our Corporate Treasury Engineering team is a world leader in developing quantitative techniques and technological solutions that solve complex and commercial business problems. We partner with our firm’s treasurer and other members of Corporate Treasury senior leadership to manage the firm’s liquidity risk, secured and unsecured funding programs, and the level and composition of consolidated and subsidiary equity capital and to invest any excess liquidity. An exciting confluence of computer science, finance and mathematics are being used to solve for what our shareholders would like from us – a high return for the right risk taken.

Your Impact

In this role, you will:

Build and deploy AI/ML models, especially those using LLMs, to solve complex business problems with large datasets.
Create scalable machine learning infrastructure, including pipelines and deployment frameworks for LLMs.
Ensure AI/ML models are accurate, reliable, and high performing, with a focus on production-level LLM solutions.
Run AI/ML experiments, refining features, prompts, and models to improve outcomes, and document key results.
Partner with researchers to integrate innovative AI/ML methods, particularly in LLMs.
Review code to uphold quality standards.
Help design and implement model monitoring and alerting systems.
Translate stakeholder needs into technical requirements, identifying opportunities for LLM-based approaches.

Basic Qualifications

B.S. or higher in Computer Science (or equivalent work experience)
3+ years of hands-on experience with building scalable machine learning systems
Experience in software development, including a clear understanding of data structures, algorithms, software design and core programming concepts.
Strong analytical and problem-solving skills – demonstrated ability to learn technologies and apply
Extensive experience with Machine Learning and Deep Learning toolkits (Tensorflow, PyTorch, Scikit-Learn, HuggingFace)
Demonstrated experience with Large Language Models (LLMs), including model fine-tuning, prompt engineering, and evaluation techniques
Experience in architecting and deploying ML applications on cloud, including containerization (Docker, Kubernetes)
Experience in working with distributed technologies like Scala, Pyspark, Iceberg, HDFS file formats (avro, parquet), AWS/ GCP, big data feature engineering
audiences and working globally.
Experience in system design and evaluating the pros and cons of database choices, schema definition for data storage.

Preferred Qualifications

Experience with Agentic Frameworks (e.g., Langchain, AutoGen) and their application to real-world problems.
Experience with model interpretability techniques.
Prior experience in code reviews/ architecture design for distributed systems.
Experience with data governance and data quality principles.
Familiarity with financial markets, financial assets, and liquidity management is plus About Goldman Sachs

Goldman Sachs Engineering Culture

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets

Engineering is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here!