MLOps Engineer Machine Learning Platform NYNJ

What We Do

At Goldman Sachs our Engineers dont 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 Machine Learning Services team.

You will be part of an expert team building and operating production-grade platform and backend systems leveraged by ML engineers and application teams across the entire firm. A key focus of this role is enabling reliable scalable and observable deployment of Machine Learning and Large Language Models (LLMs).

This role is best suited for engineers who enjoy working on infrastructure backend services and distributed systems rather than primarily on model experimentation and development.

Key Responsibilities:

• Deliver scalable efficient secure and automated processes for building deploying and monitoring 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 optimize machine learning model deployments to scale to the specific needs of each business customer

• 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

• Participate in on-call and support rotations helping diagnose and resolve production issues.

• Continuously expand knowledge of platform architecture with a goal to take ownership of individual components.

• Stay up to date with advancements in AI/ML frameworks model serving technologies and GenAI infrastructure.

Basic Qualifications:

• 2 years of experience in software engineering (backend platform or infrastructure).

• 2 years of experience in Python or a similar backend programming language.

• 1 year of experience supporting production ML systems (MLOps platform or inference-related work)

• Basic understanding of APIs (REST or similar) and service-to-service communication.

• Experience working with containers (e.g. Docker).

• Familiarity with Unix-based systems.

• Exposure to public cloud environments (e.g. AWS or GCP) including core concepts such as compute storage and basic IAM.

• Experience working with databases (SQL or NoSQL).

• Solid grasp of software engineering fundamentals including debugging testing and maintainable code design.

• Strong problem-solving skills and the ability to work effectively in a fast-paced collaborative environment.

• Curiosity and a strong desire to keep learningespecially in the model inference and LLM platform space.

Preferred Qualifications:

• 4 years of experience in software engineering (backend platform or infrastructure)

• 4 years of experience supporting production ML systems (MLOps platform or inference-related work)

• 4 years of experience in Python or a similar backend programming language.

• Strong understanding of the end-to-end Model Development Lifecycle (MDLC)

• Basic understanding of distributed systems concepts and exposure to observability concepts (logging metrics tracing).

• Experience building containerized runtime environments for model serving (e.g. vLLM SGLang TensorRT Triton AWS Multi Model Server)

• 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)

• Experience building Machine Learning models with frameworks such as PyTorch and TensorFlow

• Excellent communication skills and the ability to articulate complex technical concepts to both technical and non-technical stakeholders.

What Success Looks like in This Role:

• Can take a well-defined task and drive it to completion with minimal hand-holding.

• Asks thoughtful questions instead of getting blocked.

• Understands basic trade-offs (e.g. performance vs. simplicity flexibility vs. reliability).

• Writes code that is readable testable and easy for others to extend.

• Shows curiosity about how the entire system works end-to-end not just their assigned ticket.