Junior Ai Engineer

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.

Requirements

• 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