AI & Data Platform Engineering Intern (Undergraduate)

Contribute to Impactful Projects: Work on 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.

Build Trusted Data Foundations: 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.

Data Transformation & Pipelines: 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 solution.

AI Prototyping & Workflow Automation: Help construct and test proof-of-concept AI solutions, automated scripts, basic agentic workflows, and LLM implementations to solve everyday business problems.

Improve Data Quality & Observability: 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.

Contribute to Platform Reliability & Tooling: 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.

API & System Integration: Partner with software engineers to build integrations connecting data and AI tools with enterprise corporate systems.

Apply Modern Engineering Practices: Participate in design reviews, code reviews, architectural flow diagram drafting, automated testing, CI/CD, documentation, and production-readiness activities.

Tech Literacy & Stakeholder Enablement: 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.

Explore Emerging Technologies & Research: 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.

Collaborate & Present: Work with big data technologies and partner with engineers, researchers, product managers, and stakeholders to brainstorm solutions and present project outcomes and business impact.