LLM Intern

White Collar Realty is offering a hands-on LLM Internship in Gurgaon for aspiring AI engineers who want to work on cutting-edge large language model applications. This internship provides direct exposure to the full lifecycle of LLM-powered solutions — from prompt engineering and retrieval-augmented generation (RAG) to model evaluation and fine-tuning on proprietary, real-world content.

As an LLM Intern, you will collaborate with senior ML engineers and product teams to design, test, and optimize prompts that drive accurate, context-aware outputs. You’ll build and maintain RAG pipelines that ground model responses in verified data sources, implement automated evaluation frameworks (including human-in-the-loop and LLM-as-a-judge), and experiment with parameter-efficient fine-tuning techniques such as LoRA and QLoRA on open-source models like Llama 3, Mistral, or Phi-3.

The program emphasizes practical delivery: you’ll ship features that power internal tools and customer-facing products, with code reviews and mentorship ensuring rapid skill growth. Successful interns receive a completion certificate and are considered for a Pre-Placement Offer (PPO) based on performance and cultural fit.

We’re looking for curious, self-driven learners who thrive in a fast-paced, collaborative environment. If you’ve built side projects with LangChain, LlamaIndex, or Hugging Face Transformers, or have experimented with vector databases (Pinecone, Weaviate, Milvus) and agentic workflows, you’ll hit the ground running. This is a rare opportunity to turn academic interest in generative AI into production-grade experience at a company applying LLMs to real business problems in the real estate domain.

• Pursuing or recently completed a Bachelor’s/Master’s in Computer Science, AI/ML, Data Science, or related field (2025–2026 graduates preferred)
• Strong Python proficiency; experience with PyTorch, TensorFlow, or JAX
• Solid understanding of transformer architecture, attention mechanisms, and LLM training/inference
• Hands-on experience with prompt engineering, few-shot/chain-of-thought prompting, and prompt optimization
• Familiarity with RAG pipelines: chunking, embedding models (e.g., BGE, E5), vector stores, and retrieval strategies
• Exposure to LLM evaluation: metrics (BLEU, ROUGE, BERTScore), human evaluation protocols, and LLM-as-a-judge
• Experience with fine-tuning (LoRA/QLoRA, PEFT) on consumer GPUs or cloud (A100, H100) is a plus
• Comfortable with Git, Docker, and Linux CLI; CI/CD basics appreciated
• Excellent communication skills and ability to document experiments clearly
• Available for a 4–6 month full-time internship starting Q2/Q3 2026