AI Intern

Role & responsibilities

• Conversational AI Development: Assist in building, fine-tuning, and optimizing chatbots and conversational AI systems.
• LLM & RAG Integration: Work hands-on with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks to generate accurate, context-aware, and reliable responses.
• API Development & Integration: Design, build, and integrate robust RESTful APIs to connect AI models and chatbot interfaces with external platforms and business systems.
• ERP Data Integration: Support workflows that connect AI solutions and data pipelines with enterprise resource planning (ERP) software to automate business processes and retrieve operational data.
• Data Processing & Analysis: Perform data preprocessing, feature engineering, and Exploratory Data Analysis (EDA) to prepare and structure data for model training.
• Production & Engineering Support: Support the development, testing, and integration of AI models into scalable production systems.
• Research & Experimentation: Contribute to ongoing research and experimentation around generative AI, advanced LLM architectures, and retrieval mechanisms.
• Cross-Functional Collaboration: Collaborate closely across product, engineering, and design teams to align AI features with user experience and technical requirements.
• Documentation & Communication: Maintain clear technical documentation and present project outcomes and insights to internal stakeholders.

Preferred candidate profile

• Educational Background: Recent graduate or final-year student pursuing a degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field.
• Core Technical Knowledge: Strong foundational understanding of machine learning, deep learning, and Natural Language Processing (NLP) concepts.
• Programming Proficiency: Strong proficiency in Python and practical familiarity with ML/DL libraries such as PyTorch, TensorFlow, or Scikit-learn.
• API & Backend Familiarity: Understanding of backend development principles, web frameworks, and experience building or consuming RESTful APIs.
• Enterprise Systems Exposure (Plus): Familiarity or prior academic/project exposure to integrating applications with enterprise systems, databases, or ERP platforms.
• GenAI & Tooling Exposure: Hands-on exposure or familiarity with chatbot frameworks, LLMs (e.g., OpenAI, Hugging Face Transformers), vector databases (e.g., FAISS, Pinecone, Weaviate), and RAG pipelines.
• Data Handling Skills: Familiarity with data manipulation and visualization tools including Pandas, Matplotlib, and Seaborn.
• Desirable Extras (Nice-to-Haves):
• Experience with LLM orchestration tools like LangChain or LlamaIndex.
• Prior practical experience through chatbot projects, AI hackathons, or open-source contributions.
• Familiarity with cloud environments (AWS, GCP, Azure) and basic MLOps workflows.
• A personal GitHub or project portfolio demonstrating applied AI/NLP capabilities.