Agentic AI - Software Development Intern

Nutanix•San Jose, CA

5d•$55•Onsite

About The Position

Join our cutting-edge Engineering Team as we redefine the future of software development! We are looking for passionate, AI-driven student innovators to help us build the next generation of Coding Agents and SDLC Agents. You will have the opportunity to move beyond simple chatbots and dive into the "Agentic" era of computing. As an intern, you will gain hands-on experience with the infrastructure required to scale AI across a global engineering organization. By the end of your internship, you will have successfully designed and integrated AI agents into standard development workflows, helping us build systems that write and manage code. Engineering at Nutanix We are a fast-moving, high-impact organization focused on bringing AI to the forefront of the software development lifecycle. Our team believes that the intersection of Large Language Models (LLMs) and software engineering will fundamentally change how applications are built, tested, and deployed. We collaborate closely across research and production teams to bridge the gap between theoretical AI and production-ready developer tools. "Building the tools that build the future."

Requirements

Currently pursuing a BS (Junior or rising Senior) or MS in Computer Science, or a related technical field.
Strong proficiency in Python or Java.
Solid understanding of the Software Development Lifecycle (SDLC), modern DevOps practices, Git, unit testing, and building/consuming REST APIs.
Demonstrated passion for AI, Machine Learning, and LLM-powered applications.

Nice To Haves

Experience with agentic frameworks (LangChain, CrewAI, AutoGPT, AutoGen), vector databases (Chroma, Pinecone), or RAG architectures.
(For MS) Research experience in Automated Software Engineering (ASE), Natural Language Processing, or AI safety.

Responsibilities

Design and implement autonomous agents capable of performing tasks like automated bug fixing, code refactoring, and feature generation.
Integrate AI agents into standard development workflows, including Version Control Systems (GitHub/GitLab), CI/CD pipelines, and project management tools.
Build frameworks to benchmark agent reliability, safety, and code quality using static analysis and execution-based testing.
Experiment with advanced prompting techniques (Chain-of-Thought, ReAct) and fine-tuning strategies to optimize agent performance for coding tasks.