Software Engineer II, AI/ML Data Platforms

Socket.dev; Northern (KY); Hybrid; USD 110,000 - 150,000; Full time

Job summary

JPMorganChase is seeking a Software Engineer II within the Chief Data Analytics Office to join the AIML Data Platforms Team. You will contribute to building trusted, scalable data and analytics products in a secure environment.

The role focuses on designing and implementing cloud data solutions on AWS, Databricks, and AI-assisted development tools, with emphasis on code quality, automation, and collaboration with data engineering, ML, and application teams.

Qualifications

• Formal training or certification in software engineering concepts.
• Hands-on practical experience in system design, application development, testing, and operational stability.
• Proficiency in Python and/or Java with automated unit testing.
• Experience with AWS provisioning and cloud services.
• Experience with GitHub/Bitbucket, Jenkins, and artifact repositories.

Responsibilities

• Develops software solutions with ability to tackle non-routine problems.
• Builds data platform solutions around Data Lake tools on AWS.
• Identifies data patterns to improve code quality and architecture.
• Maintains a managed AWS Databricks platform and supports related teams.
• Collaborates across data engineering, Data Science/ML, and integration teams.
• Applies AI-assisted development tools with emphasis on secure coding standards.

Skills

Python

Java

Automated unit testing

AWS

Terraform

Spark

Databricks

AI-assisted development tools

Secure coding

Cloud data platforms

Education

Formal training or certification on software engineering concepts

Tools

GitHub/Bitbucket

Jenkins

Pypi/Maven Artifactory

Databricks

Job description

JPMorganChase is seeking a Software Engineer II within the Chief Data Analytics Office to join the AIML Data Platforms Team. You will contribute to building trusted, scalable data and analytics products in a secure environment.

The role focuses on designing and implementing cloud data solutions on AWS, Databricks, and AI-assisted development tools, with emphasis on code quality, automation, and collaboration with data engineering, ML, and application teams.