Software Engineer Internship – AI Platform Team

Wellmark•Des Moines, IA

3d•Hybrid

About The Position

Wellmark’s 2027 Summer Internship Program offers meaningful work, professional development, exposure to the health insurance industry, and opportunities to learn from subject matter experts and technology leaders. The Software Engineer Intern will join a shared project with opportunities to contribute across the AI Platform, Enterprise AI Services, and Data Engineering teams. These teams develop trusted data, governed platforms, reusable intelligence, engineering patterns, and responsible AI capabilities for technology solutions across Wellmark. Depending on project needs and interests, the intern may help build data ingestion and transformation pipelines; work with structured and semi-structured data and lakehouse layers; develop APIs, microservices, serverless components, or event-driven workflows; support AI platform capabilities and reusable AI services; and apply data quality, governance, security, privacy, and access-control considerations. The role may also involve automated testing, monitoring and troubleshooting, infrastructure-as-code, CI/CD, technical documentation, and presenting project outcomes to technical and business stakeholders. The internship starts in May 2027 and follows a hybrid schedule from Wellmark’s headquarters in Des Moines. Interns will collaborate with fellow interns, engineers, product partners, and subject matter experts on an end-to-end project. Wellmark seeks curious, collaborative problem-solvers who have a solid foundation and are eager to learn across AI, cloud, software, and data engineering.

Requirements

Be a current junior, senior, or master’s student pursuing a degree in Computer Science, Computer Engineering, Management Information Systems, Data Science, Software Engineering, Data Engineering, or a related field.
Have foundational knowledge of software development, data concepts, or cloud computing, and an interest in artificial intelligence, data engineering, platform engineering, or related disciplines.
Have experience through coursework, projects, internships, research, or personal learning with at least one programming or query language, such as Python, SQL, TypeScript, JavaScript, Java, .NET/C#, or C++.
Have a basic understanding of databases, data structures, APIs, data pipelines, cloud services, or machine learning concepts.
Demonstrate analytical, problem-solving, and critical-thinking skills and attention to detail.
Be able to troubleshoot, gather information, identify root causes, and develop practical solutions.
Have strong written and verbal communication skills and the ability to collaborate with technical and non-technical partners.
Be able to manage priorities, adapt to changing needs, work independently, and contribute effectively within a team.
Be proficient with Microsoft Office products and collaboration tools such as Microsoft Teams.

Nice To Haves

Previous internship, co-op, campus leadership role, research position, or professional work experience.
Coursework or project experience building data pipelines, ETL/ELT workflows, APIs, microservices, cloud applications, analytics solutions, or AI-enabled applications.
Exposure to AWS services, serverless or event-driven architecture, S3-based data lakes, lakehouse concepts, or distributed data processing.
Experience with Python, PySpark, SQL, Node.js, TypeScript, AWS Glue, CDK, Iceberg, Parquet, automated testing, Git, or CI/CD practices.
Exposure to generative AI, LLMs, AI agents, RAG, model evaluation, AI governance, observability, data governance, or data quality concepts.
Coursework or project exposure to AI agents, domain assistants, prompt and tool integration, knowledge retrieval, workflow automation, or reusable AI services.
Experience using modern AI tools to support software development, research, testing, documentation, or productivity.

Responsibilities

Collaborate with fellow interns, engineers, product partners, and subject matter experts to define a problem, divide work, integrate components, and demonstrate a working solution.
Build or enhance data ingestion and transformation pipelines for analytics, applications, or AI use cases.
Work with structured and semi-structured data using lakehouse and medallion concepts, including bronze, silver, and gold data layers.
Develop APIs, microservices, serverless components, or event-driven workflows that connect data and AI capabilities.
Support AI platform capabilities for safe, secure, responsible, observable, and scalable use of generative AI, large language models, agents, or related services.
Build or enhance reusable enterprise AI services, domain assistants, agents, agent skills, prompts, tools, knowledge integrations, or workflows using approved platform capabilities.
Apply data quality, governance, lineage, security, privacy, and access-control considerations throughout the solution lifecycle.
Create automated tests, monitor solution health, troubleshoot technical issues, and improve reliability and performance.
Use infrastructure-as-code and CI/CD practices to make cloud deployments repeatable, reviewable, and maintainable.
Create technical documentation, code samples, architecture diagrams, and best-practice guidance for other teams.
Present project outcomes, lessons learned, and recommendations to technical and business stakeholders.