• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
• Leverages 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; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies 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.
• Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems

• Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture

• Formal training or certification on software engineering concepts and 5+ years hands on Software Development Life Cycle experience
• Hands-on practical experience in building and maintaining testing automation frameworks with strong problem solving skills
• Hands-on building/maintaining automation frameworks at scale
• Advanced in one or more programming language(s), such as Java
• Proficiency in automation and continuous delivery methods
• Proficient in all aspects of the Software Development Life Cycle
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; 
• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

Preferred qualifications, capabilities, and skills

• Hands on experience and knowledge of financial services industry and their technology infrastructure.
• Hands on experience with Golang or RUST 
• Automated failure triage patterns: failure clustering, flaky test detection, rerun rules, quarantining workflows
• Contract testing concepts and tools 
• Component/service validation patterns 
• Building a reliable canary testing framework