Senior ML Engineer Manager — AI Platforms & Production

Capital One; Richmond (VA); On-site; USD 209,000 - 239,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer with senior experience to design, deploy, and scale ML models and data pipelines across cloud platforms. You will work with product and data science teams to deliver production-grade AI solutions and maintain governance and responsible AI practices.

The role emphasizes building multi-tenant platforms, training, serving at scale, and applying CI/CD and monitoring to ensure robust, secure, and compliant deployments.

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field.
• At least 6 years of programming experience in Python, Java, Golang, or C++ with ML libraries.
• At least 6 years of experience using and operating large scale distributed systems (Spark, Ray).
• At least 4 years deploying and operating ML solutions in production on cloud platforms and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components in collaboration with Product and Data Science teams.
• Build and scale massive multi-tenant platforms enabling training and serving at scale.
• Inform ML infrastructure decisions including model choice, data, and feature selection.
• Write, test, and deploy application code; automate tests and deployment of ML models.
• Collaborate in an Agile team to create software for large data and ML applications.
• Retrain, monitor, and maintain models in production; ensure governance and responsible AI practices.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

CI/CD

Education

Bachelor's Degree or higher in Computer Science, Machine Learning or related quantitative field

Tools

AWS

GCP

Azure

Kubernetes

Job description

Capital One is seeking a Machine Learning Engineer with senior experience to design, deploy, and scale ML models and data pipelines across cloud platforms. You will work with product and data science teams to deliver production-grade AI solutions and maintain governance and responsible AI practices.

The role emphasizes building multi-tenant platforms, training, serving at scale, and applying CI/CD and monitoring to ensure robust, secure, and compliant deployments.