Senior ML Engineer — Production AI Systems

Capital One Group; New York, Northern (NY, KY); Hybrid; USD 215,000 - 246,000; Full time

Job summary

Capital One is seeking a senior Machine Learning Engineer to design, build, and deploy production ML models in collaboration with product and data science teams.

You will work across data engineering and ops to scale ML solutions in cloud environments (AWS/GCP/Azure) with Kubernetes, CI/CD, and monitoring. Strong Python/Java/C++ experience and PyTorch or TensorFlow expertise are required.

Qualifications

• Bachelor's degree or higher in CS or a related quantitative field.
• 4+ years of experience programming with Python, Java, Golang, or C++.
• 4+ years of ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• 4+ years of experience using and operating large scale distributed systems (Spark, Ray).
• 2+ years of experience deploying ML solutions in production and operating production services 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 choices including data, features, training, and validation.
• Retrain, monitor, and maintain models in production; implement CI/CD and test automation.
• Collaborate in Agile teams to create software enabling state-of-the-art big data and ML apps.
• Develop data pipelines feeding ML models and ensure governance and security.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

scikit-learn

Spark

Ray

Kubernetes

Docker

AWS

GCP

Azure

Education

Bachelor's degree in Computer Science or related quantitative field

Tools

Spark

Ray

Kubernetes

Docker

CI/CD

Job description

Capital One is seeking a senior Machine Learning Engineer to design, build, and deploy production ML models in collaboration with product and data science teams.

You will work across data engineering and ops to scale ML solutions in cloud environments (AWS/GCP/Azure) with Kubernetes, CI/CD, and monitoring. Strong Python/Java/C++ experience and PyTorch or TensorFlow expertise are required.