Senior ML Engineer — Python, AWS & GenAI (Enterprise)

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

Job summary

Capital One in New York, NY is seeking a Machine Learning Engineer 4 to advance AI initiatives across enterprise platforms. You’ll design, build, and deploy ML models at scale, collaborating with product and data science teams in a fast-paced environment.

Key responsibilities include deploying production ML solutions on cloud platforms (AWS/GCP/Azure), building data pipelines, and ensuring model governance and reliability with CI/CD and monitoring.

Qualifications

• Bachelor's degree or higher in CS, ML, or a quantitative field.
• 4+ years programming with Python, Java, Golang, or C++.
• 4+ years ML experience with PyTorch or TensorFlow and Pandas/NumPy/Scikit-learn.
• 4+ years deploying ML in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components that solve real-world business problems, while collaborating with Product and Data Science teams.
• Inform ML infrastructure decisions including data, features, training, and validation.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate in a cross-functional Agile team to create and enhance software for big data and ML applications.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

AWS

GCP

Azure

Kubernetes

Education

Bachelor's degree or higher in CS/ML or related quantitative field

Master's or Doctoral degree (preferred)

Tools

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Capital One in New York, NY is seeking a Machine Learning Engineer 4 to advance AI initiatives across enterprise platforms. You’ll design, build, and deploy ML models at scale, collaborating with product and data science teams in a fast-paced environment.

Key responsibilities include deploying production ML solutions on cloud platforms (AWS/GCP/Azure), building data pipelines, and ensuring model governance and reliability with CI/CD and monitoring.