Software Engineer Internship – AI Platform Team

Wellmark, Inc.•Des Moines, IA

4d•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 leaders. The Software Engineer Intern will join a shared project and may contribute across the AI Platform, Enterprise AI Services, and Data Engineering teams. The teams build governed AI platforms, reusable AI services and assistants, and trusted data pipelines, products, APIs, services, and lakehouse capabilities for technology solutions across Wellmark. The internship begins in May 2027 and follows a hybrid schedule from Wellmark’s headquarters in Des Moines. Interns may collaborate with fellow interns, engineers, product partners, and subject matter experts to deliver an end-to-end solution. Potential work includes data pipelines and lakehouse solutions; APIs, microservices, serverless components, and event-driven workflows; AI platform capabilities and reusable AI services; and application of data quality, governance, security, and privacy practices. Interns may also create tests and documentation, monitor and troubleshoot solutions, use infrastructure-as-code and CI/CD practices, and present outcomes and recommendations to technical and business stakeholders. Projects are subject to change, and the employer does not expect candidates to have experience with every listed technology.

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 with 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 layers.
Develop APIs, microservices, serverless components, or event-driven workflows connecting 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.