AI ML Engineer Internship

About the Role

We are looking for a motivated AI/ML Engineer Intern passionate about Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI. This internship offers an opportunity to work on real-world AI projects, develop intelligent applications, build machine learning models, and explore AI-powered automation and autonomous AI agents.

The ideal candidate should have a strong interest in problem-solving, Python programming, data-driven solutions, and emerging AI technologies.

Key Responsibilities

• Assist in designing, developing, testing, and deploying AI/ML models for real-world applications.
• Build and optimize machine learning and deep learning models for classification, prediction, recommendation, and automation tasks.
• Develop Generative AI applications using Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG).
• Research and implement Agentic AI systems, AI agents, tool calling, multi-step reasoning workflows, and task automation.
• Work with AI frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, LangChain, or LlamaIndex.
• Perform data preprocessing, exploratory data analysis (EDA), feature engineering, and model evaluation.
• Integrate AI/ML models and LLM APIs into applications using REST APIs and Python-based backend frameworks.
• Experiment with vector databases, embeddings, semantic search, and knowledge retrieval systems.
• Collaborate with engineering and product teams to prototype, test, and improve AI-powered solutions.
• Evaluate model accuracy, performance, reliability, and response quality; troubleshoot issues and document findings.
• Stay updated on emerging trends in Artificial Intelligence, Machine Learning, Generative AI, and autonomous AI agents.

Requirements & Tech Stack

• Currently pursuing or recently completed a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Information Technology, or a related field.
• Strong programming knowledge in Python and familiarity with data structures, algorithms, and object-oriented programming.
• Basic understanding of Machine Learning concepts, including supervised learning, unsupervised learning, model training, and evaluation.
• Familiarity with Python libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib.
• Knowledge of Deep Learning, Neural Networks, NLP, or Computer Vision is an advantage.
• Understanding of Generative AI, Large Language Models (LLMs), prompt engineering, and AI application development.
• Interest in Agentic AI, AI agents, tool use, workflow orchestration, and autonomous task execution.
• Familiarity with Git, GitHub, Jupyter Notebook, APIs, and basic software development practices.
• Strong analytical thinking, problem-solving skills, communication, and willingness to learn.
• Ability to work independently and collaboratively in a fast-paced, innovation-driven environment.

Good to Have

• Hands-on projects in Machine Learning, Deep Learning, Generative AI, or AI automation.
• Experience with LangChain, LlamaIndex, Hugging Face Transformers, OpenAI APIs, or similar AI tools.
• Exposure to Retrieval-Augmented Generation (RAG), embeddings, vector databases, and semantic search.
• Familiarity with agent frameworks, function calling, multi-agent systems, and AI workflow automation.
• Basic knowledge of FastAPI, Flask, Docker, cloud platforms, or model deployment.
• A GitHub portfolio, personal AI projects, hackathon participation, or contributions to open-source projects.