Senior Lead Software Engineer, Back-End Distributed Systems (AWS, Go, Kubernetes, MCP, Lua, Python)

Overview

TheMLX (Machine Learning Experience) organization at Capital One is an internal technology division responsible for building and delivering the platforms and tools that enable the entire company to develop and deploy well-managed, responsible machine learning and Generative AI solutions.

What You’ll Do

• Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company
• Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community, and from time to time, be asked to code or evaluate code
• Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
• Utilize programming languages like JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services

Basic Qualifications

• Bachelor’s Degree
• At least 6 years of experience in software engineering (Internship experience does not apply)
• At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud)

Preferred Qualifications

• Master’s Degree
• 9+ years of experience in at least one of the following: Java, Scala, Python, Go, or Node.js
• 4+ years of experience with AWS, GCP, Azure, or another cloud service
• 4+ years of experience in open source frameworks
• 1+ years of people management experience
• 2+ years of experience in Agile practices
• 2+ years of experience with Amazon SageMaker, Databricks, or another comprehensive ML platform
• Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion