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 https://jobeax.com/link/xIvUI85pOychiV2H 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.
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