AI Automation Intern, Knowledge Graph (Internship)

Job description

About RavenPack At RavenPack, we are at the forefront of developing the next generation of generative AI tools for the finance industry and beyond. With 23 years of experience as a leading big data analytics provider for financial services, we empower our clients—including some of the world's most successful hedge funds, banks, and asset managers—to enhance returns, reduce risk, and increase efficiency by integrating public information into their models and workflows. Building on this expertise, we are launching a new suite of GenAI and SaaS services, designed specifically for financial professionals. Join a Company that is Powering the Future of Finance with AI RavenPack has been recognized as the Best Alternative Data Provider by WatersTechnology and has been included in this year's Top 100 Next Unicorns by Viva Technology. We have recently launched , a next-generation platform aimed at transforming financial decision-making. Your project You will own one or more automation projects from prototype to working tool, with the support of a dedicated technical mentor. Along the way you will: Design and build the agent, choosing the sources, tools and prompting strategies that give the most reliable results. Measure how well it works by running it against an evaluation set prepared with the team, tracking accuracy, and iterating to improve it. Produce clear, reviewable output so that the team can quickly approve, reject or adjust each proposed change. Document your work so the team can continue running and extending it after your internship. What we are looking for A builder's mindset. You are self-motivated, take ownership, and prefer shipping a working prototype to writing a long plan. You bring energy and a bias to action. Software development skills. You write clean, well-structured Python (or a similar language) and are comfortable working with APIs and data. Hands-on experience with agentic workflows. You are actively building with LLMs, agents and tool use, whether through frameworks, the Model Context Protocol (MCP), or your own orchestration code. Tell us what you have built. AI-assisted productivity. You use LLMs and AI coding tools as a daily part of how you work, and you understand both their strengths and their failure modes. Care about data quality. You understand that an automation is only useful if its output can be trusted, and you think naturally about validation and edge cases. Clear communication. You can explain what you built, why, and how well it works, in English. Nice to have Experience with knowledge graphs, entity resolution, or record linkage Familiarity with web research automation, scraping, or retrieval-augmented generation (RAG) Interest in financial markets or company data We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.