<h3>📋 Description</h3> • What “Great” Looks Like
• Prompt & RAG Engineering
• Design, tune, and A/B-test prompt chains that lift instruction-following accuracy, session-completion detection, and overall Buddy engagement.
• Implement RAG pipelines to power both support bots and in-product answers.
• Multi-language Scale-out
• Modularise Buddy prompt chains so every language pair uses a single, well-structured template with language-specific slots.
• Add capabilities to compare model performance for different languages.
• Recommendations Engine
• Prototype and productionise a Kotlin- or Python-based service that recommends the next best learning feature, measuring success via lift in feature completions and balanced usage.
• Evaluation & Observability
• Build an end-to-end prompt-quality harness (unit + offline + in-prod metrics) and integrate it with our custom model-serving layer.
• Collaboration & Ownership
• Partner daily with Sonia (Head of Content), Luis (Dir. Product Strategy & Innovation) and Elizaveta (Engineering Manager), proactively taking ownership of high-leverage deliverables that move the KPIs. <h3>🎯 Requirements</h3> • Must-Have
• Prompt-engineering best practices
• Proactiveness & strong ownership mindset
• Retrieval-Augmented Generation (RAG)
• Nice-to-Have
• Recommender-systems foundations
• A/B testing at scale
• Python (LLM tooling) & Kotlin fundamentals
• ML-ops tooling (Airflow, Feature Store)
• Basic statistics / experimentation design