Graduate AI / ML Engineer (RAG, Chatbot, LLM, Vector DB)

1–3 years of experience in ML, data science, or backend engineeringStrong Python skillsExperience building APIs or backend systemsExperience with machine learning modeling (e.g., regression, time-series, classification, or similar)Exposure to LLMs, chatbots, or prompt engineeringComfortable working with messy datasetsNice to Have: RAG or vector search experience; Time-series forecasting (Prophet, XGBoost, etc.); Retail / supply chain data experience; MLOps or production ML exposureWhy Join: Build real AI products (not just models); Work on LLMs, chatbots, and predictive ML systems; High ownership and fast growth; Be part of a major platform rebuildCompensation: Competitive salary; Health benefits; Hybrid work modelOptional (but high leverage): Please include examples of ML models or LLM projects you’ve built (GitHub or portfolio). Build AI agents (“Krunchy”) that generate Insights, Reports, RecommendationsDevelop RAG pipelines combining LLMs with structured data (POS, inventory, product data)Create chat-based experiences for customer analyticsMachine Learning Modeling (Core): build and improve models for Demand forecasting, Stockout risk, Lost sales estimation, Anomaly detectionPerform feature engineering on messy retail datasetsModel evaluation and iterationHelp take models from prototype → productionTech Stack: Python (FastAPI preferred), LLM APIs (OpenAI, Anthropic), LangChain / LlamaIndex (or similar), Vector databases, ClickHouse / modern data stack, AWS / cloud infrastructure Krunchbox is a retail analytics SaaS platform helping brands increase sell-through, prevent stockouts, and uncover lost revenue across major retailers. We’re launching Krunchbox Reimagined — a modern AI platform focused on predictive analytics, AI agents, and real-time decision support. We’re looking for an AI / ML Engineer (1–5 years experience) to help us build chatbots, RAG systems, and production-grade ML models used directly by customers. This role spans building AI agents that generate insights, reports, and recommendations; developing RAG pipelines blending LLMs with structured data (POS, inventory, product data); and creating chat-based experiences for customer analytics. Competitive compensation package.Comprehensive health and benefits coverage.A predominantly in-person, collaborative work environment located in Santiago to encourage fast iteration and real-time problem solving.Opportunity to scale and lead a global SaaS platform that solves real-world customer challenges.A direct, impactful role in shaping the future of AI-powered supplier-retailer collaboration. RAG or vector search experienceTime-series forecasting (Prophet, XGBoost, etc.)Retail / supply chain data experienceMLOps or production ML exposure