Intern - Trainee Engineer

Location: Bangalore
Duration: 3 months

About the Role:

We are looking for a passionate and motivated AI/ML Intern to join our team. In this role, you will work closely with our Data Science and Engineering teams to develop, train, and deploy machine learning models that solve real-world problems.  This is a hands-on opportunity to gain experience with cutting-edge technologies—including Generative AI, Large Language Models (LLMs), AI Agents, and Retrieval-Augmented Generation (RAG)—and contribute to impactful projects across domains.

Key Responsibilities:

• Assist in collecting, cleaning, and preprocessing structured and unstructured data for ML models
• Build and experiment with machine learning models for classification, regression, clustering, or NLP tasks
• Perform exploratory data analysis and visualize insights
• Support the deployment and evaluation of models in a production or simulated environment
• Collaborate with cross-functional teams including data engineers and software developers
• Document experiments, results, and workflows
• Stay updated with the latest trends and research in AI/ML, including LLMs, Generative AI, AI Agents, and retrieval-based techniques
• Explore the application of LLMs (e.g., GPT, Claude, LLaMA) in areas such as summarization, question answering, and content generation
• Contribute to prototyping and evaluation of Retrieval-Augmented Generation (RAG) pipelines using tools like LangChain or LlamaIndex
• Support prompt engineering, chaining, and evaluation of agent-based systems for specific business tasks.

Roles & Responsibilities

Preferred Skills & Qualifications:

• Pursued a degree in Computer Science, Data Science, AI/ML, or related field
• Strong foundation in Python and libraries such as NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch
• Basic understanding of GenAI API usage concepts (e.g., OpenAI, Anthropic, Hugging Face). 
• Familiarity with LLM frameworks such as LangChain or LlamaIndex is a plus.
• Understanding of foundational ML concepts including supervised/unsupervised learning, embeddings, and transfer learning.
• Familiarity with vector databases like FAISS, Pinecone, or ChromaDB is an advantage
• Familiarity with SQL and data visualization tools (e.g., Matplotlib, Seaborn, Plotly)
• Knowledge of NLP or deep learning is a plus
• Excellent problem-solving and communication skills
• Eagerness to learn, experiment, and work independently as well as in collaborative teams