Core Engineering, Machine Learning Platform, MLOps Engineer, Analyst/ Associate, Singapore

What We Do  

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, which is comprised of our Technology Division and global strategists’ groups, 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. 

 

Who We Look For 

We are seeking a skilled and motivated engineer to join our Artificial Intelligence Platforms organization as an MLOps Engineer on our Experimentation and Model Development offering. In this role, you will be part of an expert team responsible for our firmwide JupyterHub implementation on Kubernetes which supports our most advanced GPU based user environments. These environments are a critical component of the firms Generative AI agenda as they provide the ability to experiment and fine tune foundation models, along with building agentic AI applications. 

 
Key Responsibilities: 

• Deliver scalable, efficient, secure and automated processes for building, training and fine-tuning Machine Learning models 
• Enable solutions that provide business customers with the ability to leverage the latest and greatest AI/ML infrastructure, frameworks, and tooling to deliver high impact outcomes 
• Develop and demonstrate deep subject matter expertise on how to implement distributed GPU computing 
• Deliver high quality, production ready code leveraging CI/CD best practices 
• Author and maintain high quality documentation for both the engineering team as well as for business customers 
• Remain up to date with the latest advancements in AI/ML frameworks and related technologies 

 

Basic Qualifications: 

• 2+ years (for Associate)/ 1+ year (for Analyst) of experience in building production software using Python 
• 1+ years of experience as an ML Ops Engineer supporting building, training and/or fine-tuning models 
• 1+ years of experience building/maintaining JupyterHub or JupyterLab implementations 
• 1+ years of experience implementing solutions on Kubernetes 
• 1+ years of experience with Unix-based systems 
• 1+ years of experience delivering solutions in a public cloud (e.g. AWS, GCP) 
• Strong desire to keep learning and stay up to date with the latest and greatest developments in the machine learning domain, especially Large Language Models (LLMs) 
• Strong problem-solving skills and the ability to work effectively in a fast-paced and collaborative environment 

 

Preferred Qualifications: 

• Strong understanding of the end-to-end Model Development Lifecycle (MDLC) 
• Strong understanding of Python frameworks, packages and tools 
• Experience building Machine Learning models with frameworks such as PyTorch and TensorFlow  
• Experience with infrastructure-as-code tools, such as Terraform or CloudFormation 
• Experience with Kubernetes and other container orchestration platforms in the public cloud (e.g. AWS, GCP) 
• Excellent communication skills and the ability to articulate complex technical concepts to both technical and non-technical stakeholders. 

We are seeking a skilled and motivated engineer to join our Artificial Intelligence Platforms organization as an MLOps Engineer on our Experimentation and Model Development offering. In this role, you will be part of an expert team responsible for our firmwide JupyterHub implementation on Kubernetes which supports our most advanced GPU based user environments. These environments are a critical component of the firms Generative AI agenda as they provide the ability to experiment and fine tune foundation models, along with building agentic AI applications.