Data Engineering intern (Sprinternship)

LexisNexis Risk SolutionsBoca Raton, FL
Onsite

About The Position

LexisNexis® Risk Solutions provides customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. We use the power of data and advanced analytics to help our customers make better, timelier decisions. By bringing clarity to information, we ultimately help make communities safer, insurance rates more accurate, commerce more transparent, business decisions easier and processes more efficient. You can learn more about LexisNexis Risk at the link below, https://risk.lexisnexis.com About This Role: As a Data Engineering intern, you will contribute to the design and development of a shared, production‑ready AI foundation, including a validated prompt library, modular sub‑agents, and reusable skills that support common engineering and QA use cases. Location: On-site in Boca Raton, FL. Relocation assistance is not provided. Program Dates: May 4 – June 26, 2026 Eligibility: Undergraduate students expected to graduate by December 2026

Requirements

  • 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

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.
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