Software Engineer II – Python

Overview

In this Software Engineer II role within Infrastructure Platforms, you contribute to secure, scalable software in a large enterprise. You work as part of an agile team to design, develop, and troubleshoot software components for mission-critical technology products. You leverage AI-assisted development tools to improve code quality and speed, while ensuring secure coding standards and peer-reviewed outputs. You will tackle problems across the SDLC, collaborating with cross-functional teams to deliver stable, resilient solutions. This opportunity offers growth within a leading financial services technology organization and a chance to shape complex systems at scale.

Responsibilities

• Execute standard software design, development, and troubleshooting tasks
• Write secure, high-quality code using at least one programming language with limited guidance
• Leverage enterprise-authorized AI coding tools to improve quality, speed, and productivity with peer review and automated testing
• Design, develop, code, and troubleshoot with consideration of upstream/downstream systems and technical implications
• Apply SDLC toolchain knowledge, including AI-assisted development and automation, to increase value
• Apply technical troubleshooting to resolve basic-to-moderate complexity problems
• Gather and analyze large data sets to inform secure, stable application development decisions
• Learn and apply system processes, methodologies, and skills for secure, stable code and systems

Key requirements

• Formal training or certification on software engineering concepts and advanced 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
• Hands-on experience using enterprise-authorized AI-assisted software development tools with ability to critically evaluate AI outputs
• Understanding of responsible AI use in engineering workflows, including data sensitivity and security considerations
• Experience across the full Software Development Life Cycle
• Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security

• Modern programming languages (one or more)
• Database querying languages
• AI-assisted development tools (enterprise-approved)
• Secure coding practices
• Knowledge of SDLC toolchains and automation
• Data analysis for decision-making

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