AI & Data Platform Engineering Intern (Undergraduate)

WEXWEXUS•Remote, OH

3d•Hybrid

About The Position

WEX is seeking a motivated undergraduate student in Computer Science, Data Science, Software Engineering, Information Systems, or a related field to join our Data, AI, and Risk Technology (DART), Semantic Foundation, and Enterprise Applications engineering teams as an intern. This 13-week paid internship program, running from late May to mid-August, offers a chance to gain real-world experience and develop skills in cutting-edge AI/ML applications, Big Data platforms, agentic systems, and Enterprise Semantic Models within the FinTech industry. Interns will support key business functions across Fraud and Credit Risk, Sales, Service, Marketing, HR, Finance, and Legal. Working alongside experienced engineers and researchers, interns will tackle real-world data engineering and platform challenges using modern technologies such as Python, SQL, Airflow, dbt, PySpark, Snowflake, cloud platforms, and LLMs. The team is global and remote-first (spanning the U.S., India, and Brazil), operating with agile sprint disciplines and maintaining a strong culture of ownership, learning, and cross-functional collaboration. High-performing interns may have the chance to lead their own initiatives. WEX offers remote, hybrid, and onsite options for flexibility, with the goal of bringing top interns back full-time or offering them extended roles during the school year.

Requirements

Currently pursuing a Bachelor's degree in Computer Science, Machine Learning, Software Engineering, or related field and will have completed at least your Sophomore year of school at the start of the internship.
Proficiency in coding using modern languages (preferably Python, Java, or Go) along with Object-Oriented Programming (OOP) concepts, scripting, automated testing, system monitoring, and basic familiarity with version control tools (e.g., Git/GitHub).
Solid understanding of machine learning algorithms, techniques, best practices, or a strong academic interest in Artificial Intelligence, Machine Learning, and Natural Language Processing.
Hands-on experience working with data, including data analysis, manipulation, and visualization, alongside an academic background or practical experience with SQL and relational database concepts.
Strong analytical thinking, adaptability, and the ability to break down problems, investigate issues, evaluate possible solutions, and seek guidance when appropriate.
Desire to learn robust design solutions for small to medium-scale problems or system components.
Excellent written and verbal communication skills to clearly articulate technical ideas, share progress, ask questions, and collaborate effectively with team members across different locations and time zones.
Curiosity, high motivation, and willingness to quickly learn new technologies, platforms, and business domains—including proactively utilizing GenAI-assisted development tools to streamline workflows, accelerate learning, and enhance productivity.
Willingness to take responsibility for an assigned problem and drive it toward a meaningful outcome rather than focusing only on individual tasks.
Enthusiasm for working with big data technologies, AI/ML, risk analysis, enterprise software engineering, system design, and cloud infrastructure.

Nice To Haves

Hands-on exposure to Airflow, dbt, PySpark, Spark, or similar data engineering tools.
Familiarity with Snowflake, AWS, Azure, GCP, or other cloud-based data platforms and data warehouses.
Exposure to Data Quality, Observability, monitoring, or platform reliability engineering.
Familiarity with DevOps practices and tools, CI/CD, automated testing, APIs, event-driven systems, or distributed systems.
Interest in GenAI, agentic applications, or semantic technologies, as well as contributions to open-source projects or publications in relevant conferences.
Hands-on experience gained through academic projects, hackathons, prior internships, research, personal/self-directed coding projects, or open-source contributions involving data engineering or software development.
A strong desire to solve customer and business problems through thoughtful, effective, and user-friendly engineering solutions.

Responsibilities

Contribute to challenging projects that leverage AI/ML to address critical business needs in areas like fraud prevention, credit risk assessment, and real-time, data-driven decision making.
Contribute to semantic models and reusable data assets that provide consistent business entities, definitions, and context for analytics, applications, and AI use cases across the enterprise.
Develop advanced platforms, SQL queries, and tools to process, ingest, clean, enrich, and optimize core company data—leveraging consuming services, microservices, and DAGs to unlock value for both customers and internal teams.
Design and implement AI-assisted workflows that directly optimize our production environment. Apply specification-driven development and declarative system design to build highly scalable, model-driven solutions.
Help construct and test proof-of-concept AI solutions, automated scripts, basic agentic workflows, and LLM implementations to solve everyday business problems.
Help implement automated data-quality checks, monitoring, alerting, and observability capabilities that improve the accuracy, completeness, freshness, and reliability of critical data platforms and pipelines.
Work on engineering improvements, reusable frameworks, automated testing, and CI/CD to make data platforms more resilient, scalable, easier to operate, and productive for developers.
Partner with software engineers to build integrations connecting data and AI tools with enterprise corporate systems.
Participate in design reviews, code reviews, architectural flow diagram drafting, automated testing, CI/CD, documentation, and production-readiness activities.
Partner directly with non-technical business users and stakeholders to simplify complex technical concepts, guiding them on how to effectively integrate data and AI tools into their everyday workflows through hands-on support and documentation.
Research new AI tools, open-source libraries, GenAI, agentic applications, and AI-assisted development tools to present findings, demos, and lessons learned to the broader engineering team.
Work with big data technologies and partner with engineers, researchers, product managers, and stakeholders to brainstorm solutions and present project outcomes and business impact.