Senior ML Engineer - Python, AWS, GenAI, Scale Production

Capital One; Plano (TX); On-site; USD 179,000 - 205,000; Full time

Job summary

Capital One is seeking a Machine Learning Engineer to design, build, and deploy ML models at scale. You’ll collaborate with Product and Data Science teams to deliver production-ready ML components and data pipelines in a fast-paced, Agile environment.

You will train, monitor, and retrain models, applying best practices in CI/CD, testing, and governance to ensure robust, scalable AI solutions across cloud-based platforms.

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field.
• At least 4 years of experience programming with Python, Java, Golang, or C++.
• At least 4 years of Machine Learning experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 4 years of experience using and operating large scale distributed systems (Spark, Ray).
• At least 2 years of experience deploying and operating Machine Learning solutions in production in the cloud (AWS, GCP, Azure) and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions including model/data/feature choices and validation.
• Write and test application code, develop/validate ML models, and automate tests/deployment.
• Collaborate in a cross-functional Agile team to enable state-of-the-art ML apps.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.
• Construct data pipelines to feed ML models.
• Ensure code quality, governance and Responsible AI practices.

Skills

Python

Java

Golang

C++

ML experience

Distributed systems

Education

Bachelor's Degree in CS or related field

Master's or Doctoral Degree in CS/EE/Math

Tools

PyTorch

TensorFlow

Spark

Ray

Kubernetes

Job description

Capital One is seeking a Machine Learning Engineer to design, build, and deploy ML models at scale. You’ll collaborate with Product and Data Science teams to deliver production-ready ML components and data pipelines in a fast-paced, Agile environment.

You will train, monitor, and retrain models, applying best practices in CI/CD, testing, and governance to ensure robust, scalable AI solutions across cloud-based platforms.