Senior MLOps Engineer Scalable ML in Production

Capital One; McLean (VA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One is seeking a Lead Machine Learning Engineer to drive MLOps practices, build and optimize ML pipelines, and deploy scalable models on AWS. The role emphasizes design, production readiness, and governance across data platforms.

You will collaborate with Product and Data Science teams to deliver robust ML solutions, mentor teams, and ensure reliability, security, and performance in a fast-paced agile environment.

Qualifications

• Bachelor’s degree required, internship not considered
• At least 6 years designing & building data‑intensive solutions using distributed computing
• At least 4 years programming with Python, Scala or Java
• At least 2 years building, scaling, and optimizing ML systems

Responsibilities

• Design, build, and deliver ML models and components for production systems
• Inform ML infrastructure decisions including model choice, data, features, and training
• Write and test application code, validate ML models, automate tests and deployment
• Collaborate in a cross-functional Agile team to enable state‑of‑the‑art ML apps
• Retrain, maintain, and monitor models in production
• Leverage cloud architectures to deploy ML models at scale
• Construct optimized data pipelines for ML models
• Adopt CI/CD with test automation and monitoring
• Ensure code, models, and governance meet Responsible AI standards
• Use Python, Scala, or Java for development

Skills

MLOps

Kubernetes

PyTorch

TensorFlow

Python

Scala/Java

AWS Cloud

Education

Bachelor's degree

Master's or Doctoral degree (preferred)

Tools

scikit-learn

Spark

Dask

Job description

Capital One is seeking a Lead Machine Learning Engineer to drive MLOps practices, build and optimize ML pipelines, and deploy scalable models on AWS. The role emphasizes design, production readiness, and governance across data platforms.

You will collaborate with Product and Data Science teams to deliver robust ML solutions, mentor teams, and ensure reliability, security, and performance in a fast-paced agile environment.