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LearnWise AI

AI Engineering Intern (LLM Ops & RAG)

12 hours ago

Córdoba

LearnWise is a company where the best idea always wins — no matter what — and where innovation and hyper-growth are in our DNA. We offer Engineering internships designed to build future technical leaders - hopefully at LearnWise! Internship · EU · Remote Department: Engineering Reports to: Head of AI Work alongside: CTO, AI team, Engineering & Product Job Description LearnWise.ai is a scale-up modernizing educational institutions with virtual assistants, instructor efficiency, and student engagement solutions for higher education. We're seeking an inspired and rigorous Engineering Intern for a 6-month internship focused on AI systems, reliability, and product behavior. You'll join a VC-funded team with a track record of exponential growth. You'll work closely with leadership and be expected to propose, test, and ship your own ideas. What you'll do AI Systems & Reliability • Troubleshoot LLM behavior and agent actions across our Tutor Assistant, automatic feedback, and other AI features; identify root causes and ship fixes., • Use LangSmith, LogFire and other tools to trace requests end-to-end, correlate prompts, tools, and outputs, and explain why a behavior occurred., • Optimize our retrieval pipelines: choose and tune embeddings, refine vector-database indexing/search strategies, and evaluate re-rankers for higher precision/recall., • Contribute to LLM Ops: prompt/tool versioning, dataset curation, regression test suites, latency/cost monitoring, and safe rollout strategies (shadow, A/B)., • Build small utilities to surface failure taxonomies, detect drift, and turn traces into actionable fixes., • Partner with the Head of AI, CTO, AI team, Engineering, and Product to shape technical direction., • Strong proficiency in Python., • Master's (ongoing or completed) in AI/ML or a related field, or equivalent experience demonstrating comparable depth., • Solid understanding of modern AI models and AI-powered applications (prompting, tool-use/agents, RAG, context management, evaluation)., • Hands-on experience with LLMs and/or tool-using agents: debugging prompts, tracing tool calls, analyzing failure modes., • Ability to design evaluation loops (golden sets, regression tests, online experiments) that make reliability measurable and improvable., • Experience optimizing RAG components: embeddings selection/tuning, vector DB configuration, re-ranker selection/evaluation., • Familiarity with AI observability/monitoring practices and failure taxonomies., • Direct mentorship from senior leadership (CTO, Head of AI, Head of Product)., • Work with a global team on cutting-edge AI applications., • Flexible working arrangements (results > hours).