Middle/Senior AI Engineer

LLM & Conversational AI:End-to-End RAG Architecture: Design, build, and optimize robust RAG pipelines using LangChain to orchestrate data, models, and logic.Data Ingestion & Processing: Develop workflows for ingesting and processing unstructured data.Embedding & Vector Search: Implement and manage embedding strategies using models from OpenAI and Google (Gemini), leveraging vector stores like Chroma, Redis, Elasticsearch, and PostgreSQL.LLM Integration & Orchestration: Integrate and manage multiple LLMs (Vertex AI, Azure OpenAI, OpenRouter,...), optimizing for performance, cost, and capability.Evaluation & Alignment: Champion continuous improvement by using LangSmith for observability and Guardrails AI to ensure AI outputs are safe, reliable, and on-brand.Deployment: Collaborate with our platform team to containerize (Kubernetes) and deploy AI services on Azure, ensuring scalability and efficiency.Product Recommendation & Personalization:Algorithm Development: Build and optimize recommendation models using techniques like collaborative filtering, content-based filtering, matrix factorization, and explore hybrid approaches.Feature Engineering: Engineer and select impactful features from user behavior data (clicks, purchases, browsing patterns) and product catalogs to train personalization models.Model Lifecycle Management: Own the end-to-end lifecycle of recommendation models, from data preprocessing and training to offline/online evaluation (A/B testing) and production monitoring.System Integration: Work with backend engineering teams to integrate recommendation model outputs into the live e-commerce platform via APIs.Innovation: Research and apply novel techniques, including leveraging LLMs to enhance recommendations (e.g., for cold-start problems or generating natural language explanations for recommendations).