LLM & RAGS intern

Role Overview

We are seeking a highly motivated AI/ML Intern with a focus on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).  You will be working at the frontier of generative AI, helping us explore, build and optimize systems that bridge the gap between static models and dynamic, private data.

This role is ideal for a student looking to apply theoretical knowledge to a production-grade environment and build a significant portfolio piece.

Key Responsibilities

Architect & Optimize RAG Pipelines: Improve retrieval accuracy using vector databases (e.g., Pinecone, Milvus, or Weaviate).

LLM Implementation: Experiment with and fine-tune prompts and workflows using models like GPT-4, Claude, or Llama 3.

Data Engineering: Assist in the cleaning, chunking, and embedding of proprietary datasets.

Evaluation: Implement benchmarking frameworks to measure hallucinations, faithfulness, and relevancy of model outputs.

Qualifications

Academic Standing:  Graduate student (Masters/PhD) in Computer Science, Data Science, or a related technical field.

GPA: 3.5 or higher preferred.

Technical Skills: Proficiency in Python and experience with AI frameworks (e.g., LangChain, LlamaIndex, PyTorch, or TensorFlow).

Domain Knowledge: A solid understanding of transformer architectures and the mechanics of RAG.

Soft Skills: A research-oriented mindset with the ability to troubleshoot complex, non-deterministic systems.

What You Will Gain

Mentorship: Direct access to several AI chief architects and weekly 1-on-1 growth sessions.

Portfolio Impact: Significant contribution to a live AI project that you can showcase to future employers.

Flexibility: We respect your academic schedule and offer flexible working hours.

Future Opportunities: Top performers will be prioritized for future full-time, paid openings.