Data Machine Learning Engineer

• Maintain and develop ML serving pipelines using Kubeflow, Spark, and Python.
• Collaborate with Data Science teams on training pipelines and feature engineering.
• Develop features, deploy applications, perform testing, and implement vulnerability fixes.
• Debug and provide support for production ML pipelines and CI/CD workflows.
• Support integration efforts across various groups and the enterprise.
• Build, train, and deploy machine learning models.
• Support models related to credit card decisioning, fraud tracking, and risk assessment.

Python

• AWS

• Kubernetes

• Kubeflow (or equivalent workflow experience)

• Spark pandas, NumPy

• ML Ops / ML tooling experience

• Hybrid on-site requirement (must be able to work in-office; McLean preferred, New York possible)

• Previous Capital One experience highly desirable

Nice to haves:

• SQL / data analysis experience

• Databricks

• Additional ML tooling experience (mlplot, Data bricks)

• DevOps familiarity (Jenkins, CICD pipelines)

• AWS solution Architect Cert, * Experience with MLOps and ML tooling.
• Proficiency in Python.
• Knowledge of Kubernetes and AWS.
• Experience with Kubeflow or equivalent workflow tools.
• Familiarity with Spark, pandas, and NumPy., * Previous experience with the client is desirable.
• Experience with SQL and data analysis.
• Familiarity with Databricks.
• Knowledge of additional ML tooling, such as mlplot.
• Understanding of DevOps concepts, including Jenkins and CI/CD pipelines.
• An AWS Solution Architect Certification is considered an asset.