Senior Lead ML Engineer - Production-Scale AI Architect

capitalone; Chicago (IL); On-site; USD 209,000 - 239,000; Full time

Job summary

Capital One is seeking a Senior Lead Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design and build ML models, guide ML infrastructure decisions, and ensure robust deployment and monitoring of models and software.

You will collaborate across Product, Data Science, and Engineering teams, retrain and monitor models in production, and leverage cloud platforms to deliver optimized ML solutions at scale.

Qualifications

• Bachelor’s degree required; advanced degrees preferred.
• Experience building production ML solutions and scalable data pipelines.
• Experience leading ML teams and communicating complex concepts.
• Experience in cloud environments (AWS/Azure/GCP).
• Experience with CI/CD for ML deployments.

Responsibilities

• Design, build, and deliver ML models and components in collaboration with Product/Data Science teams
• Inform ML infrastructure decisions including model choice, data and feature selection
• Write, test, and deploy application code; develop and validate ML models
• Collaborate in an agile team to create software for big data and ML apps
• Retrain, monitor, and maintain models in production
• Leverage cloud architectures to deliver ML models at scale
• Construct data pipelines feeding ML models; ensure CI/CD and monitoring

Skills

Distributed computing

Python

Scala/Java

ML systems

Team leadership

Education

Bachelor's Degree

Master's or Doctoral Degree

Tools

PyTorch

TensorFlow

scikit-learn

Spark

Dask

Job description

Capital One is seeking a Senior Lead Machine Learning Engineer to join an Agile team focused on productionizing ML applications at scale. You will design and build ML models, guide ML infrastructure decisions, and ensure robust deployment and monitoring of models and software.

You will collaborate across Product, Data Science, and Engineering teams, retrain and monitor models in production, and leverage cloud platforms to deliver optimized ML solutions at scale.