Senior ML Engineer 4: GenAI, Python & AWS at Scale

Capital One National Association; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One National Association is seeking a Machine Learning Engineer 4 to design, build, and scale ML models and data pipelines across EPTech. You will collaborate with Product and Data Science teams in an Agile environment to deliver production-ready ML solutions.

The role requires advanced Python/Java/Golang/C++ programming, hands-on experience with PyTorch/TensorFlow, distributed systems (Spark/Ray), and cloud deployments (AWS/GCP/Azure) with Kubernetes.

Qualifications

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

Responsibilities

• Design, build, and deliver ML models and components to solve real-world business problems with Product and Data Science teams.
• Inform ML infrastructure decisions: model choice, data, feature selection, training, tuning, validation.
• Collaborate in cross-functional Agile teams to create and enhance software for ML applications.
• Retrain, monitor, and maintain models in production with governance and responsible AI practices.

Skills

Python

Java

Golang

C++

Python ML

PyTorch

TensorFlow

Spark

Kubernetes

Education

Bachelor's degree in CS or related field

Master's degree preferred

PhD beneficial

Tools

Pandas

NumPy

Scikit-learn

Spark

Ray

Job description

Capital One National Association is seeking a Machine Learning Engineer 4 to design, build, and scale ML models and data pipelines across EPTech. You will collaborate with Product and Data Science teams in an Agile environment to deliver production-ready ML solutions.

The role requires advanced Python/Java/Golang/C++ programming, hands-on experience with PyTorch/TensorFlow, distributed systems (Spark/Ray), and cloud deployments (AWS/GCP/Azure) with Kubernetes.