Senior ML Engineer: Scalable Production & Cloud

ITSMF; New York (NY); On-site; USD 251,000 - 286,000; Full time

Job summary

Capital One is seeking a senior Machine Learning Engineer to design, train, and deploy advanced ML models in production. You will work closely with Product and Data Science teams to deliver scalable, secure AI solutions across multi-tenant platforms.

The role emphasizes building robust data pipelines, monitoring models in production, and leveraging cloud-based architectures (AWS/GCP/Azure) with Kubernetes for scalable ML software systems.

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field.
• At least 6 years of experience programming with Python, Java, Golang, or C++.
• At least 6 years of Machine Learning experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 6 years operating large scale distributed systems (Spark, Ray).
• At least 4 years deploying and operating ML solutions in production and cloud environments (AWS, GCP, Azure) with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components solving real-world business problems with Product and Data Science teams.
• Scale multi-tenant platforms for training and serving ML models at scale.
• Inform ML infra decisions, including model choice, data, feature selection, training, and validation.
• Write and test application code, develop and validate ML models, automate tests and deployment.
• Collaborate in an Agile team to create software for state-of-the-art big data and ML apps.
• Retrain, maintain, and monitor models in production; ensure governance and Explainable AI.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Education

Bachelor's Degree or higher in Computer Science or related quantitative field

Tools

Kubernetes

AWS

GCP

Azure

Job description

Capital One is seeking a senior Machine Learning Engineer to design, train, and deploy advanced ML models in production. You will work closely with Product and Data Science teams to deliver scalable, secure AI solutions across multi-tenant platforms.

The role emphasizes building robust data pipelines, monitoring models in production, and leveraging cloud-based architectures (AWS/GCP/Azure) with Kubernetes for scalable ML software systems.