Senior ML Engineer: Python, AWS, GenAI

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

Job summary

Capital One is seeking a Machine Learning Engineer to design and deploy production ML models, collaborating with Product and Data Science teams on scalable solutions. You will shape ML infrastructure, data pipelines, and model governance while ensuring responsible AI practices.

The role emphasizes building scalable ML systems, training and validating models, and deploying them in cloud environments using Kubernetes and modern CI/CD practices.

Qualifications

• Bachelor’s degree in Computer Science, Machine Learning or related quantitative field.
• At least 4 years of programming experience in Python, Java, Golang or C++.
• At least 4 years of Machine Learning experience using PyTorch or TensorFlow and libraries such as Pandas, NumPy, Scikit-learn.
• At least 4 years of experience with large-scale distributed systems (Spark, Ray) to prepare AI/ML data.
• At least 2 years of deploying ML solutions in production and using Kubernetes for containerized ML systems.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions: data, features, model choice, training, tuning, validation.
• Develop and test production code, automate tests and deployment for ML models.
• Collaborate in cross-functional Agile teams to enable scalable ML applications.
• Retrain, monitor, and maintain models in production settings.
• Leverage cloud architectures to scale ML models and data pipelines.

Skills

Python

Java

Golang

C++

Education

Bachelor's degree in CS/ML or related field

Tools

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Job description

Capital One is seeking a Machine Learning Engineer to design and deploy production ML models, collaborating with Product and Data Science teams on scalable solutions. You will shape ML infrastructure, data pipelines, and model governance while ensuring responsible AI practices.

The role emphasizes building scalable ML systems, training and validating models, and deploying them in cloud environments using Kubernetes and modern CI/CD practices.