Applied AI ML Associate

About the Role

As an emerging member of a software engineering team, you will execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role.

Key Responsibilities

• Executes standard software solution, design, development, and technical troubleshooting.
• Writes secure and high-quality code using the syntax of at least one programming language with limited guidance.
• 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.
• 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, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Applies technical troubleshooting to break down solutions and solve technical problems of basic complexity.
• 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.

Requirements & Tech Stack

• Formal training or certification on software engineering concepts and 2+ 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.
• 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.
• Experience across the whole Software Development Life Cycle.
• Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security.

Qualifications

Preferred Qualifications

• Experience adopting AI-native engineering practices at team or organizational scale.
• Experience leading adoption of agentic AI-enabled engineering practices across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
• Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
• Track record of reducing operational toil and cost through automation and better engineering.