AI/ML Engineering Internship

Join our team as an AI/ML Engineering Intern and enhance our ML/LLMOps and AI Agent infrastructure. You will evaluate and implement different LLM frameworks, AI agent architectures, and 3rd party APIs. Additionally, you will design and experiment with AI agent evaluation systems, support the development and automation of AI agent systems and Retrieval-Augmented Generation (RAG) pipelines, and document your findings in our internal knowledge base. This is a remote position with opportunities for personal and professional growth.

• Enhance ML/LLMOps and AI Agent infrastructure for model evaluation, testing, and deployment within AWS.
• Evaluate and implement different LLM frameworks, AI agent architectures, and 3rd party APIs.
• Design and experiment with AI agent evaluation systems to measure reasoning quality, reliability, and consistency.

• Fluency in English (written & spoken)
• Excellent academic background in a quantitative discipline (computer science, machine learning, data science, financial mathematics,, engineering, physics, or a similar field)
• Experience with ML packages in Python (e.g., pandas, numpy, scikit-learn, statsmodels)
• Currently pursuing a Bachelor’s or Master’s degree
• Please also provide us with your latest transcript of records and if applicable your work references
• Hands on experience in Python Fundamental knowledge in state-of-the-art LLM frameworks, RAG and vector databases (e.g. LangChain, LangGraph,, OpenSearch)
• First experiences with Cloud Infrastructure (AWS)
• Result-oriented and pragmatic way of working
• Curiosity and enthusiasm to learn new technologies and build reliable AI systems
• First-hand experience with creating LLM-powered applications, AI agents, tool use, and multi-step reasoning workflows. (e.g. Chatbots, information retrieval, document summarization)
• Experience with AI Agent or RAG Evaluation Systems (e.g. LLM-as-a-judge, automated scoring pipelines, or Langfuse-based evaluation)
• Good understanding of software engineering principles, including API development, code versioning, and testing frameworks (e.g. Pytest, FastAPI, etc.)
• Familiarity with end-to-end ML projects
• Experience with Infrastructure as Code (IaC) like terraform is a plus
• Experience with CI/CD frameworks (e.g. Jenkins, Gitlab CI/CD) is a plus