Opportunity brief
AI/ML Engineer Intern – Roles, Responsibilities & RequirementsAbout the RoleWe 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.
Roles & 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:
- 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.
What You Will Gain
- Hands-on experience building real-world AI/ML and Generative AI applications.
- Practical exposure to LLM integration, RAG pipelines, and Agentic AI development.
- Opportunities to experiment with modern AI tools, frameworks, and deployment techniques.
- Experience collaborating on AI product development, prototyping, testing, and optimization.
- Portfolio-building opportunities through practical projects and industry-relevant problem-solving.