Role Description You will take ownership of the search infrastructure for Ask Seeking Alpha — a financial intelligence platform powered by LLMs. Your main goal is to ensure our AI agents can find the exact financial data they need (articles, transcripts, news) in milliseconds. You will work on the Retrieval layer of our RAG architecture, combining traditional text search with modern vector search techniques. Tech Stack: - Elasticsearch (v8+) - Python (FastAPI, Asyncio) - OpenAI Embeddings - LangChain - LangSmith The Senior Search Engineer will be engaged with the company as a contractor (Private Entrepreneur). What You'll Do - Search Engine Development: Design and implement Hybrid Search strategies. You will figure out how to best combine "keyword matching" (finding specific tickers like 'AAPL') with "semantic search" (finding concepts like 'revenue growth'). - Relevance Tuning: You are responsible for the quality of search results. You will build systems to measure and improve how well the search engine answers user queries (using tools like LangSmith). - Vector Search & RAG: Manage the integration of OpenAI embeddings into Elasticsearch. You will solve challenges related to indexing long documents (e.g., earnings transcripts) so the AI retrieves only the most relevant parts. - Performance Optimization: Optimize Elasticsearch queries and index settings to ensure low latency, even for complex queries with many filters. - Python Backend: Develop and maintain the Python services that build queries and process results. We use FastAPI and Asyncio heavily. Qualifications - 5+ years of experience working with Search Engines in production. - Understanding of how indices, analyzers, and mappings work "under the hood." - Understanding the difference between Lexical Search (keywords) and Vector Search (meaning), and know when to use which. - Strong experience with Python 3.10+. - Comfortable writing asynchronous code (async/await) and building APIs. - Experience designing data schemas for search (how to structure JSON documents for efficient retrieval). Requirements - Experience building RAG (Retrieval-Augmented Generation) pipelines. - Familiarity with LangChain or similar LLM frameworks. - Experience with Evaluation tools (like LangSmith) to test search quality automatically. - Background in Finance (understanding tickers, earnings calls, etc.).