AI Intern

Role Overview

Brainwonders is looking for enthusiastic AI Interns who are passionate about working with cutting-edge technologies, learning professional AI development practices, and gaining real-world exposure in generative AI and agentic systems. This is a 6-month internship with the possibility of transitioning to a full-time role.

Responsibilities

• Build and test LLM-powered applications, AI agents, and autonomous workflows.
• Design and implement agentic AI systems capable of planning, reasoning, tool use, and task execution.
• Develop multi-agent systems where multiple specialized agents collaborate to solve complex tasks.
• Experiment with agent frameworks such as LangGraph, CrewAI, LangChain, and other emerging agentic AI frameworks.
• Implement agent memory, state management, task decomposition, planning, routing, and orchestration.
• Integrate agents with external tools, APIs, databases, search systems, and other services using function/tool calling.
• Explore Model Context Protocol (MCP) and other approaches for connecting AI agents with external tools and data sources.
• Use and adapt transformer-based models such as LLaMA, Mistral, and other open-source or proprietary LLMs for domain-specific applications.
• Build RAG pipelines using embeddings and vector databases for knowledge retrieval and contextual reasoning.
• Deploy AI models, agents, and supporting services on AWS (Lambda, EC2, S3, SageMaker, etc.).
• Work on prompt engineering, structured outputs, fine-tuning, model selection, and pipeline optimization.
• Develop evaluation strategies to measure agent accuracy, reliability, latency, cost, and task completion.
• Implement monitoring, logging, and debugging for LLM and agentic workflows.
• Collaborate with the team to prototype and productionize AI copilots, autonomous workflows, and intelligent automation systems.

Requirements

• Strong understanding of LLMs, tokenization, transformers, embeddings, and generative AI fundamentals.
• Good Python programming skills.
• Familiarity with tools/libraries such as Pandas, NumPy, Hugging Face, LangChain, LangGraph, or CrewAI.
• Understanding of AI agent concepts, including tool calling, planning, reasoning, memory, state, and orchestration.
• Basic understanding of multi-agent architectures and how multiple agents can collaborate or delegate tasks.
• Familiarity with RAG, vector databases, semantic search, and embeddings.
• Exposure to AWS services such as EC2, Lambda, S3.
• Basic knowledge of machine learning and hands-on project experience.
• Ability to build and debug end-to-end AI applications.
• Strong curiosity and willingness to experiment with rapidly evolving agentic AI technologies.

Skills

• Python
• LangChain
• AWS
• LLMs
• RAG