Python- AI Software Engineer III

JPMorganChase•Wilmington, NC

About The Position

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you. As a Software Engineer III at JPMorgan Chase within the Corporate Sector, you will design, build, and maintain scalable backend data engineering solutions that power critical financial reporting and analytics capabilities across the firm. You will work closely with cross-functional teams to deliver high-quality, reliable data pipelines and platform modules that support data-driven financial decision-making at enterprise scale. Your work will directly influence how the firm processes, manages, and leverages financial data to serve millions of customers and stakeholders every day.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience.
Hands-on practical experience in system design, application development, testing, and operational stability.
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages.
Proficiency in Python backend development with hands-on experience building and optimizing PySpark data pipelines in production environments.
Demonstrated experience with Databricks, including module development, deployment, and ongoing operational support.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Solid understanding of databases (Snowflake, Databricks, SQL, DB2 or NOSQL).

Nice To Haves

Experience with Apache Spark, Kafka, or similar distributed data processing frameworks in a large-scale enterprise environment.
Exposure to cloud-native development and AWS services, particularly AWS EMR, Glue, ECS, EKS (Elastic Kubernetes Service) for deploying and managing containerized applications.
Exposure to AI/ML data platforms.

Responsibilities

Execute software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
Create secure and high-quality production code and maintain algorithms that run synchronously with appropriate systems, with a focus on Python backend development and PySpark-based data processing.
Build, deploy, and support scalable data engineering modules on Databricks, ensuring reliable performance, maintainability, and alignment with platform architecture standards.
Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contribute learnings and reusable patterns to improve broader team effectiveness.
Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Produce architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development.
Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse financial data sets in service of continuous improvement of software applications and systems.
Proactively identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture.
Participate in code reviews, providing and incorporating constructive feedback to continuously elevate code quality and engineering standards across the team.
Collaborate with product managers, architects, and cross-functional engineering teams to translate finance business requirements into scalable technical solutions.