Lead ML Engineer (MLOps) - Kubernetes & Cloud Infra

Capital One; Richmond (VA); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a Lead Machine Learning Engineer (MLE) to productionize ML applications at scale. You’ll work on ML architectural design, build models and pipelines, and ensure high availability and performance within an Agile team.

In this role you will apply languages like Python/Scala/Java, build scalable data pipelines, and leverage cloud-native ML platforms such as KServe, Kubernetes, PyTorch, and TensorFlow on AWS. The position emphasizes responsible AI and robust, maintainable code.

Qualifications

• Bachelor’s degree required.
• 6+ years designing data-intensive solutions with distributed computing.
• 4+ years programming in Python/Scala/Java.
• 2+ years building ML systems.

Responsibilities

• Design, build, and deliver ML models and components with Product/Data Science teams.
• Inform ML infrastructure decisions on data, model, and features.
• Write and test application code, train models, and automate tests/deployment.
• Collaborate in an Agile team to enable ML apps at scale.
• Retrain, monitor, and maintain models in production.

Skills

Python/Scala/Java

Distributed computing

ML engineering

Education

Bachelor's degree

Tools

KServe

Kubernetes

PyTorch

TensorFlow

AWS

Job description

Capital One is seeking a Lead Machine Learning Engineer (MLE) to productionize ML applications at scale. You’ll work on ML architectural design, build models and pipelines, and ensure high availability and performance within an Agile team.

In this role you will apply languages like Python/Scala/Java, build scalable data pipelines, and leverage cloud-native ML platforms such as KServe, Kubernetes, PyTorch, and TensorFlow on AWS. The position emphasizes responsible AI and robust, maintainable code.