Senior ML Engineer: AI Production at Scale (Python/AWS)

Capital One; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One seeks a Machine Learning Engineer 4 to design, deploy, and maintain production ML models at scale in McLean, VA. You will collaborate with Product, Data Science, and Ops to build ML-enabled platforms and data pipelines using Python, Spark, and cloud services.

The role requires distributed systems experience (Spark/Ray), cloud deployment (AWS/GCP/Azure), and containerization (Kubernetes). Strong collaboration, CI/CD, and Responsible AI practices are essential.

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 4 years of experience programming with Python, Java, Golang, or C++; ML experience with PyTorch or TensorFlow
• At least 4 years of experience using large-scale distributed systems (Spark, Ray) to prepare AI/ML data
• At least 2 years of experience deploying and operating ML in production in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems

Responsibilities

• Design, build, and deliver ML models and components in collaboration with Product and Data Science teams
• Inform ML infrastructure decisions including model, data, and feature selection
• Retrain, maintain, and monitor models in production
• Construct optimized data pipelines to feed ML models
• Use CI/CD and test automation for deployment of ML models and application code

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

AWS

GCP

Azure

Kubernetes

CI/CD

Docker

Education

Bachelor's degree in CS/ML or related quantitative field

Master's or Doctoral degree (preferred)

Tools

Docker

Kubernetes

Spark

Job description

Capital One seeks a Machine Learning Engineer 4 to design, deploy, and maintain production ML models at scale in McLean, VA. You will collaborate with Product, Data Science, and Ops to build ML-enabled platforms and data pipelines using Python, Spark, and cloud services.

The role requires distributed systems experience (Spark/Ray), cloud deployment (AWS/GCP/Azure), and containerization (Kubernetes). Strong collaboration, CI/CD, and Responsible AI practices are essential.