Responsibilities
- Develop Multi-Agent Architectures: Collaborate with senior engineers to design and implement systems where multiple AI agents coordinate to execute complex workflows.
- Engineer Robust Frameworks: Build and refine agent communication patterns, memory management systems, and tool-usage protocols to ensure reliability and scalability.
- System Integration: Integrate LLMs with external APIs, databases, and existing enterprise infrastructure to create seamless automation loops.
- Reliability & Scaling: Assist in creating orchestration layers that manage failure modes gracefully and ensure consistent performance in a production environment.
- Innovation & Best Practices: Contribute to internal codebases and help establish engineering standards for agentic development within the organization.
Basic qualifications
- Currently pursuing a Master’s degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related technical field.
- Proven Experience: Demonstrated history of building multi-agent systems (via internships, research, or significant personal projects).
- Technical Proficiency: Strong programming skills in Python with an emphasis on writing clean, maintainable, production-quality code.
- Framework Knowledge: Hands-on experience with agent orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen, Semantic Kernel, or custom-built solutions).
- LLM Expertise: Deep understanding of Large Language Model capabilities, context management, and advanced prompting strategies.
Preferred qualifications
- Active contributions to open-source AI or agent-based repositories.
- Familiarity with asynchronous programming, message queues, and distributed systems concepts.
- Experience implementing Retrieval-Augmented Generation (RAG) architectures and utilizing vector databases.
- Knowledge of evaluation metrics and observability tools for monitoring agent performance.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Internship Ai Ai Engineer