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
Required qualifications, capabilities, and skills
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 Python, React.JS programming languages and database querying languages
Experience with testing framework including Playwright/Pytest
Hands-on experience using enterprise-authorized AI-assisted software development tools (GitHub, Copilot, Claude Code) 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.
Overall knowledge of the Software Development Life Cycle
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Preferred qualifications, capabilities, and skills
Experience working in a payment processing domain
Familiarity with modern front-end technologies
Exposure to cloud technologies
Familiarity with large language models (LLMs) including ChatGPT, Google Bard, and related platforms.
Exposure to frameworks and techniques such as LangChain, Retrieval-Augmented Generation (RAG), and fine-tuning of LLMs.