AI Application Development Intern - Summer 2026
Lenovo
Software Engineering, Data Science
Morrisville, NC, USA
Why Work at Lenovo
Description and Requirements
AI Application Development Intern - Summer 2026
About the Role:
We are looking for a motivated AI Application Development Intern who is excited to build real-world AI solutions and gain hands-on experience with modern AI engineering tools. In this role, you will support the development of conversational agents, retrieval-augmented generation (RAG) pipelines, and large language model (LLM)–driven applications.
You will work closely with experienced engineers and architects to design, prototype, test, and improve AI-powered components. This internship is well suited for recent graduates or advanced students who have strong technical foundations and are ready to apply them to practical, production-oriented AI projects while continuing to develop their skills.
What You Will Do:
AI & Agent Development
- Assist in designing and developing AI agents and conversational chatbots for business and technical use cases.
- Implement LLM-based workflows using frameworks such as LangChain, LangGraph, or similar tools.
- Contribute to prompt design, structured prompting strategies, and prompt optimization to improve reliability and performance.
Retrieval-Augmented Generation (RAG)
- Help build and refine RAG pipelines, including data ingestion, embedding generation, indexing, and retrieval.
- Work with vector databases and retrieval frameworks to enable knowledge-aware AI agent behaviors.
Application & Prototype Engineering
- Develop Python-based prototypes and small-scale AI applications that integrate LLMs and APIs.
- Support backend development activities, such as APIs, integration scripts, and automation pipelines.
- Participate in rapid prototyping and experimentation with new AI capabilities, tools, and architectures.
Testing, Evaluation & Improvement
- Assist in evaluating model accuracy, reliability, and performance across AI components.
- Write and maintain tests for AI workflows, agents, and services.
- Debug issues, analyze results, and collaborate on iterative improvements.
Research & Collaboration
- Stay informed on emerging AI tools, frameworks, and best practices in applied AI development.
- Collaborate with engineers, architects, and product owners to explore new ideas and solutions.
- Participate in design reviews, demos, and innovation sessions.
Required Qualifications:
Technical Skills
- Experience with Python programming, including scripting or data handling.
- Familiarity with at least one AI or LLM development framework such as LangChain, LangGraph, or similar.
- Basic understanding of LLM concepts, embeddings, vector search, or model inference.
- Experience building small applications, scripts, or AI prototypes through coursework, personal projects, or internships.
Mindset & Working Style
- Strong interest in applied AI engineering and AI agent systems.
- Ability to work independently on well-defined technical tasks.
- Willingness to experiment, iterate, and learn from feedback.
- Clear communication skills and ability to collaborate in a team environment.
Preferred Qualifications (Nice to Have)
- Experience with Azure, Azure AI services, OpenAI APIs, or other LLM platforms.
- Coursework or hands-on experience in machine learning, natural language processing (NLP), or software engineering.
- Exposure to web development, backend services, or API integration.
What You Will Gain
- Practical, real-world experience building AI applications and intelligent agent systems.
- Exposure to modern LLM development workflows, RAG architectures, and prompt engineering techniques.
- Mentorship from experienced AI engineers and architects.
- Opportunities to contribute to meaningful prototypes and innovation initiatives.
- A stronger technical portfolio demonstrating applied AI engineering work.
Discipline: AI Application Development, Software Engineering
This role is ideal for candidates who are excited to move from theory to practice and want to help shape how AI solutions are designed, built, and evaluated in real production environments.