AI/ML Intern (Generative AI & LLM)

About the RoleWe are looking for a technically strong and highly motivated AI/ML Intern with a genuine interest in Machine Learning, Generative AI, and Large Language Models.This is a hands-on internship focused on building real AI/ML applications using Python, LLMs, RAG, LangChain, embeddings, vector databases, NLP, and Machine Learning. Candidates should have strong fundamentals, practical project experience, and the ability to independently research, experiment, debug, and learn new technologies.Key ResponsibilitiesDevelop AI/ML and Generative AI applications using Python.Build LLM-powered applications using LangChain, LLM APIs, prompt engineering, and RAG.Develop RAG pipelines involving document processing, chunking, embeddings, retrieval, and response generation.Work with LLMs such as OpenAI, Gemini, Claude, Llama, or similar models.Work with Machine Learning, Deep Learning, and NLP problems.Perform data preprocessing, cleaning, transformation, and analysis.Experiment with prompts, embedding models, retrieval strategies, and LLM parameters.Evaluate model and LLM performance using relevant accuracy, relevance, quality, and latency metrics.Work with vector databases and semantic search.Build backend APIs for AI applications using FastAPI or Flask.Integrate AI solutions with databases and external APIs.Investigate and improve issues such as hallucinations, irrelevant retrieval, poor responses, and latency.Write clean, modular, and maintainable Python code.Use Git and follow software development best practices.Document experiments, technical approaches, and evaluation results.Stay updated with developments in Generative AI, LLMs, AI Agents, and NLP.Required SkillsStrong programming skills in Python.Good understanding of Machine Learning fundamentals, including training, validation, overfitting, and model evaluation.Understanding of Deep Learning concepts and exposure to PyTorch or TensorFlow.Familiarity with NLP concepts such as tokenization, embeddings, transformers, and semantic similarity.Strong conceptual understanding of LLMs and Generative AI.Hands-on exposure to at least one LLM API such as OpenAI, Gemini, Claude, or similar.Understanding of prompt engineering, RAG, embeddings, semantic search, and vector databases.Hands-on exposure to LangChain is strongly preferred.Familiarity with REST APIs and FastAPI/Flask.Basic knowledge of SQL and databases.Familiarity with Git and GitHub/GitLab.Strong analytical, debugging, and problem-solving skills.Preferred SkillsExperience with LangGraph or AI Agents.Experience with vector databases such as ChromaDB, FAISS, Pinecone, Weaviate, or Milvus.Exposure to Hugging Face Transformers or open-source LLMs.Experience building RAG-based applications or AI-powered chatbots.Familiarity with Docker and cloud platforms such as AWS, Azure, or GCP.Exposure to MLflow or similar experiment-tracking tools.Understanding of hallucination reduction, guardrails, LLM evaluation, and responsible AI.Technical ExpectationsThe candidate may be evaluated through practical assignments involving:Building a basic RAG application.Implementing document chunking, embeddings, retrieval, and generation.Designing and evaluating prompts.Building a Python API around an AI/ML application.Debugging an AI/ML pipeline.Training and evaluating a Machine Learning model.Identifying and improving hallucination or retrieval issues.What We Are Looking ForWe are looking for candidates with strong fundamentals and genuine hands-on interest, not candidates who have only followed tutorials or used LLM APIs without understanding the underlying concepts.The ideal candidate should demonstrate:Strong Python and programming fundamentals.Good understanding of AI/ML concepts.Curiosity and willingness to go beyond surface-level knowledge.Ability to independently research and troubleshoot problems.Strong logical and analytical thinking.Ability to explain technical decisions clearly.Willingness to experiment, learn, and take ownership.Core Technical StackProgramming: PythonAI/ML: Machine Learning, Deep Learning, NLP, PyTorch / TensorFlowGenerative AI: LLMs, Prompt Engineering, RAG, Embeddings, AI AgentsFrameworks: LangChain, LangGraphVector & Data: ChromaDB, FAISS, Pinecone, Weaviate, SQLBackend: FastAPI, Flask, REST APIsCloud & DevOps: AWS / Azure / GCP, Docker, CI/CDVersion Control: Git, GitHub / GitLabQualificationsPursuing or completed a Bachelor's/Master's degree in Computer Science, AI/ML, Data Science, Engineering, or a related field.Strong academic or practical foundation in programming and AI/ML.Demonstrated interest through projects, research, hackathons, certifications, or independent experimentation.Strong communication and collaboration skills.Ability to learn quickly and work in a fast-paced technical environment.