Senior ML Engineer (Python, AWS, GenAI)

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

Benefits offered by this job

Performance-based incentives

Benefits package

Job summary

Capital One National Association is seeking a Machine Learning Engineer 4 to design, build, and productionize ML models at scale using Python, AWS, SQL, and GenAI. You will collaborate with Product and Data Science teams to deliver enterprise AI solutions.

This role emphasizes production deployment, MLOps, cloud infrastructure, and responsible AI with CI/CD, monitoring, and governance as core practices. A cross‑functional agile team supports rapid iteration while ensuring reliability.

Qualifications

• Bachelor's degree or higher in CS or a related quantitative field.
• 4+ years programming with Python, Java, Golang, or C++.
• 4+ years ML experience with PyTorch or TensorFlow.
• 4+ years deploying ML on large scale distributed systems (Spark, Ray).
• 2+ years deploying ML in production on AWS, GCP, or Azure with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components for real‑world business problems.
• Inform ML infrastructure choices including data, features, and model selection.
• Write, test, and deploy production ML software and automate tests.
• Collaborate in an agile cross‑functional team to enable big data and ML apps.
• Retrain, monitor, and maintain models in production; ensure governance and bias controls.
• Leverage cloud architectures to deliver ML models at scale; build data pipelines.
• Apply CI/CD, monitoring, and security best practices for reliable deployments.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

SQL

GenAI

Education

Bachelor's degree in CS or related field

Tools

Spark

Ray

Kubernetes

AWS

GCP

Azure

Pandas

NumPy

Scikit-learn

Job description

Capital One National Association is seeking a Machine Learning Engineer 4 to design, build, and productionize ML models at scale using Python, AWS, SQL, and GenAI. You will collaborate with Product and Data Science teams to deliver enterprise AI solutions.

This role emphasizes production deployment, MLOps, cloud infrastructure, and responsible AI with CI/CD, monitoring, and governance as core practices. A cross‑functional agile team supports rapid iteration while ensuring reliability.