Mlops Engineer – Model Inference For Llms (Toronto)

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
Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.
We are seeking a talented and experienced MLOps Engineer to join our Data Science and Machine Learning Platform team. As part of our team, you will collaborate with business customers and NLP engineers on the implementation of real-time ML/AI Models, including Large Language Models (LLMs), within our cloud-based, firmwide inference service. This person will also be responsible for continuing to build out net-new capabilities for our inference service to further optimize the environment to support state-of-the-art models. This is an incredible opportunity to drive impactful and high-profile business value while working with the latest and greatest ML/AI frameworks and technologies.
Key Responsibilities

• Develop, implement and maintain Data Science and Machine Learning platform solutions for model management, deployment, inferencing and monitoring leveraging open-source technologies
• Understand and implement techniques for optimizing LLM inference speeds without sacrificing accuracy
• Leverage your background in Machine Learning and Software Engineering as you partner w/business customers to optimize and deploy real-time models in an operational setting, leveraging MLOps best practices

Basic Qualifications

• Bachelor’s (or equivalent experience) or Master's degree in Computer Science, Engineering or related field
• 1+ year of experience designing and implementing ML infrastructure to support the Model Development Lifecycle
• 1+ years of experience in Python programming for machine learning and/or application development
• 2+ years of experience delivering and maintaining software solutions in production environments
• 1+ years of experience with Unix-based systems and working with command-line tools
• 1+ years of experience in containerization and deployment using Docker with a container orchestration solution

Preferred Qualifications

• Knowledge and hands-on experience with Hugging Face Transformers library
• Experience deploying Machine Learning Platform solutions using Kubernetes
• Knowledge of techniques to tune open-source LLMs, along with how to improve inference speeds while maintaining accuracy
• MLOps experience supporting the deployment and ongoing maintenance of real-time models in an operational setting
• Previous experience building real-time interactive applications, such as chat or messaging platforms
• Excellent communication skills to effectively interact with business stakeholders and technical team
• Experience leveraging AWS SageMaker for hosting ML models
• Strong problem-solving skills and ability to work independently and collaboratively within a team
• Experience with Triton, TensorRT and ONNX
• Experience deploying and managing applications in the Public Cloud (e.g. AWS, GCP)

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, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
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