Role Overview
Role Description
We are seeking a highly motivated AI Engineer Intern with a strong background in Generative AI to join our team. In this role, you will work on building, experimenting with, and deploying cutting-edge AI solutions powered by Large Language Models (LLMs) and generative technologies. You will collaborate closely with AI engineers and product teams to transform ideas into intelligent, real-world applications.
What You’ll Do
As an AI Engineer Intern, you will contribute to the development of LLM-powered systems, including chatbots, AI agents, and retrieval-augmented generation (RAG) solutions. You will assist in prompt engineering, model evaluation, fine-tuning, and integration of generative models into backend services. You will also work with embeddings, vector databases, and AI frameworks to improve system accuracy, reliability, and performance.
What You’ll Gain
This internship offers hands-on experience with production-level Generative AI, mentorship from experienced AI engineers, and exposure to real business use cases. You will gain practical skills in LLM orchestration, AI agents, and MLOps best practices while working in a fast-paced, innovation-driven environment.
Who Should Apply
This role is ideal for students or recent graduates who are passionate about Generative AI, eager to learn, and excited to work on modern AI technologies that have real-world impact.
Requirements
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field
- Strong understanding of Generative AI concepts (LLMs, transformers, diffusion models)
- Hands-on experience with Large Language Models (LLMs) such as GPT-based or open-source models
- Experience with prompt engineering and prompt optimization techniques
- Familiarity with RAG (Retrieval-Augmented Generation) architectures and vector databases
- Proficiency in Python for AI and data-related tasks
- Experience using PyTorch or TensorFlow for model experimentation
- Knowledge of NLP fundamentals (tokenization, embeddings, attention mechanisms)
- Experience with embedding models and semantic search
- Familiarity with tools like LangChain, LlamaIndex, or similar frameworks
- Basic understanding of model fine-tuning (LoRA, adapters, or parameter-efficient tuning)
- Experience integrating LLMs with APIs and backend services
- Familiarity with Git and collaborative development workflows
- Understanding of AI safety, hallucinations, and evaluation techniques
- Strong curiosity, fast learning ability, and passion for Generative AI