Validate performance of RAG-based LLM models using real field escalation scenarios
Analyze and structure internal data sources including logs, knowledge base, and support documentation
Develop and optimize retrieval pipelines including embeddings and indexing strategies
Improve prompt engineering techniques to enhance response accuracy and relevance
Benchmark RAG models against baseline LLM approaches
Identify data gaps and recommend improvements for knowledge coverage
Collaborate with software, applications, and product teams to align solutions with business needs
Document findings and present results to stakeholders

Currently pursuing a Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field
Strong programming skills in Python
Understanding of machine learning, NLP, or large language models
Familiarity with data analysis and problem-solving techniques
Strong communication and documentation skills
Preferred: Experience with LLM frameworks (e.g., OpenAI, LangChain, Hugging Face)
Preferred: Knowledge of vector databases (e.g., FAISS, Pinecone)
Preferred: Experience with Retrieval-Augmented Generation (RAG) concepts
Preferred: Exposure to cloud platforms (Azure preferred)

AI/ML Internship

Key skills