Data Engineering intern (Sprinternship) at LexisNexis… · Boca Raton

Summary: LexisNexis Risk Solutions provides advanced technology and analytics to assist customers in evaluating risk. They are seeking a Data Engineering intern to contribute to the design and development of AI foundations, focusing on creating reliable and scalable AI-enabled workflows. Responsibilities: - Collaborate with teammates to define and apply shared standards and conventions for prompts, templates, formatting, and system structure to ensure consistency, quality, and scalability across AI‑enabled workflows - Implement validation and testing approaches to evaluate prompt and agent outputs for reliability, repeatability, and correctness across multiple scenarios and use cases - Iteratively refine AI artifacts based on testing results, feedback from engineering and QA stakeholders, and evolving project requirements - Produce clear, well‑structured technical documentation—including standards, usage guides, examples, and onboarding materials—to support users ranging from novice to advanced levels - Apply sound software engineering practices such as version control, modular design, and incremental delivery to ensure solutions are maintainable and extensible over time - Participate in team discussions, design reviews, and knowledge‑sharing sessions to align on best practices and ensure a shared understanding of responsible, effective AI usage - Deliver project milestones that progress from initial concept and baseline artifacts to a validated, production‑ready solution suitable for broader organizational adoption Required Qualifications: - Be currently pursuing a bachelor's degree in computer science or a related field of study with a graduation date of December 2026 - General-purpose programming (e.g., Python or JavaScript) for scripting, prototyping, and light automation - Version control and collaboration tools (Git/GitHub), including basic workflow integration - Prompt engineering and modular system design (prompts, sub‑agents, reusable skills) - Validation and testing concepts to ensure consistent, repeatable outputs - Technical documentation skills, including writing clear guides, standards, and usage examples - Analytical and problem-solving skills to break down ambiguous problems into structured solutions - Clear written and verbal communication, especially for explaining technical concepts to varied audiences - Collaboration and feedback integration across engineering and QA stakeholders - Attention to quality, standards, and consistency - Learning agility and comfort working with evolving AI-enabled workflows Required Skills: Python, JavaScript, Version control, Git, GitHub, Prompt engineering, Modular system design, Validation, testing, Analytical, Clear written, Attention to quality, standards, consistency, Learning agility, feedback integration