AI Engineering Intern

AI Engineering Intern

Summer 2026  ·  10–12 Weeks ·  Junior, Senior, or Graduate Student Welcome

Python / LLMs / RAG/ Agentic AI Data Pipelines/ MLOps

This is not a research internship where you read papers and write summaries. At iAdeptive, AI Engineering interns work directly alongside our Lead AI Engineer on production federal programs — building pipelines, training and evaluating models, and contributing code that runs in real environments. You will leave with a portfolio of shipped work, not just slides.

🎯  WHAT YOU'RE SIGNING UP FOR

Real federal programs. Real data. Real code reviews. You will use the same tools as the team — Cursor, Claude Code, Copilot, Docker, Git — and contribute to AI systems that have to be explainable, auditable, and responsible. The bar is higher than a class project. That's the point.

⚙️  HANDS-ON FROM DAY ONE

Your first task is assigned by end of Week 2. Your code gets reviewed by senior engineers. You ship something real before Week 10. We do not believe in internships where the main deliverable is a PowerPoint. If you want to build, this is your environment.

📈  RETURN OFFER PIPELINE

Top-performing interns are considered for full-time roles as iAdeptive grows. 

What You Will Work On

• Build and test Retrieval-Augmented Generation (RAG) pipelines using real federal program data
• Implement and evaluate agentic AI workflows using LangChain, LangGraph, or CrewAI under senior guidance
• Develop prompt engineering strategies, evaluation harnesses, and automated quality checks for LLM outputs
• Build and maintain data pipelines — preprocessing, feature engineering, and data transformation for ML workflows
• Contribute to MLOps tooling: experiment tracking, model versioning, observability dashboards, and automation scripts
• Create data visualizations and analytics outputs to support model evaluation and client insights
• Work with Docker, Git, REST APIs, and cloud platforms (AWS, Azure, or GCP) as part of the standard engineering workflow
• Support development of model cards, system cards, and AI use-case documentation for client programs
• Participate in architecture reviews, code reviews, and technical design discussions
• Present your work at a final intern demo — to iAdeptive leadership and, where appropriate, to federal clients

What We're Looking For

• Currently pursuing a BS, MS, or PhD in Computer Science, AI, Data Science, or a closely related field
• Strong Python skills — you have built something with it, not just completed tutorials
• Familiarity with ML fundamentals: training, evaluation, overfitting, embeddings, and model workflows
• Exposure to data pipelines, preprocessing, and ML libraries (scikit-learn, PyTorch, TensorFlow, or equivalent)
• Working knowledge of Git, Docker, and REST APIs — standard tools you are already comfortable using
• Curiosity about LLMs and generative AI — you follow the space and have opinions about where it is heading
• Strong written communication — you can explain what you built, why it works, and what the trade-offs were

Bonus Points

• Hands-on experience with LLM APIs (OpenAI, Anthropic, Hugging Face, or equivalent)
• Prior experience with vector databases (Pinecone, Weaviate, Chroma, pgvector)
• Familiarity with agentic frameworks: LangChain, LangGraph, CrewAI, or AutoGen
• Experience with cloud ML services: AWS SageMaker, Azure ML, or Google Vertex AI
• Personal or academic projects using GenAI — shipped and working, not just started
• Interest in federal technology, public sector AI, or responsible and ethical AI
• Graduate students with ML research experience or published work are especially encouraged to apply

10–12 Week Intern Journey

Weeks

Focus & Deliverables

Weeks 1–2

Onboarding, codebase orientation, federal program context, tooling setup (Cursor, Docker, Git, cloud environment). First task assigned by end of Week 2.

Weeks 3–5

Core project delivery — RAG pipeline implementation, data pipeline development, or agentic workflow component. Daily standups, weekly 1:1 with Lead AI Engineer.

Weeks 6–8

Iteration and integration — code reviews, model evaluation, testing, and documentation. Visualization and analytics outputs for client programs.

Weeks 9–10

Project completion and polish. Code merged to main branch. Written technical summary and model/system card delivered.

Weeks 11–12

Demo day presentation to iAdeptive leadership. Return offer conversations for top performers. Knowledge transfer and handoff documentation.

Why iAdeptive

We support interns with mentorship, structured onboarding, and weekly check ins.  Most internships give you toy problems. We give you real ones. Federal AI is one of the most complex and consequential domains in engineering — high-stakes data, governance requirements, and systems that need to work the first time. If you want to graduate knowing how production AI actually gets built and deployed, this is the internship.