Software Engineer II - AIML Experimentation and Governance

Job responsibilities

• Executes standard software solutions, design, development, and technical troubleshooting
• Writes secure and high-quality code using the syntax of at least one programming language with limited guidance
• Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications
• Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation
• Develop secure high-quality production code, and review and debug code written by others.
• Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development
• Learns and applies system processes, methodologies, and skills for the development of secure, stable code and systems
• Adds to team culture of diversity, opportunity, inclusion, and respect
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
• 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

Required qualifications, capabilities, and skills

• Proven production oriented software engineering 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
• Demonstrable ability to code in one or more languages
• Experience across the whole Software Development Life Cycle
• Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security
• Emerging knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
• Experience with coding assistants and development harnesses to streamline software delivery.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.

Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations.

Preferred qualifications, capabilities, and skills

• Familiarity with modern front-end technologies
• Exposure to cloud technologies
• Awareness to AIML/GenAI tools and platforms.
• Strong problem-solving, analytical, and communication skills.