Senior AI Engineer

Description

Summary: Join Seeking Alpha as a Senior Python AI Engineer specializing in Generative AI to design and optimize complex financial analysis systems using LLMs and advanced agent orchestration. Highlights: 1. Develop cutting-edge financial analysis systems with Large Language Models. 2. Design and implement complex agent orchestration logic using LangGraph. 3. Opportunity to work with advanced AI technologies and optimize LLM interactions. **Join a Company That Invests in You** Seeking Alpha is the world’s leading community of engaged investors. We’re the go\-to destination for investors looking for actionable stock market opinions, real\-time market analysis, and unique financial insights. At the same time, we’re also dedicated to creating a workplace where our team thrives. We’re passionate about fostering a flexible, balanced environment with remote work options and an array of perks that make a real difference. Here, your growth matters. We prioritize your development through ongoing learning and career advancement opportunities, helping you reach new milestones. Join Seeking Alpha to be part of a company that values your unique journey, supports your success, and champions both your personal well\-being and professional goals. **What We're Looking For** **Role Overview:** We are developing **Ask Seeking Alpha** — a high\-load financial analysis system based on Large Language Models. The architecture is built on complex multi\-agent orchestration using **LangGraph**, **FastAPI**, and **Elasticsearch**. We are looking for a Senior Python AI Engineer specialized in Generative AI to design agent workflows, optimize interactions with models (OpenAI, AWS Bedrock), and ensure the reliability of non\-deterministic systems in production. **Tech Stack:** Python (Asyncio), FastAPI, LangChain, LangGraph, Pydantic, Elasticsearch, AWS Bedrock / OpenAI API, LangSmith. Requirements: * **Python Expert:** Strong proficiency in modern Python. Deep understanding of **asynchronous programming (asyncio)** patterns is mandatory, as our entire I/O pipeline (Network, DB, LLM) is non\-blocking. Experience with FastAPI and Pydantic (v2\). * **Agentic Frameworks:** Production experience with **LangChain**. Hands\-on experience or deep conceptual understanding of **LangGraph** (or similar state\-machine based agent frameworks). **Deep LLM Expertise (What we mean by "Deep"):** * **Non\-determinism Management:** Strategies for handling LLM hallucinations and ensuring reliable outputs (e.g., self\-correction loops, specific prompting techniques like CoT/ReAct). * **Structured Outputs:** Experience forcing LLMs to adhere to strict schemas (Pydantic/JSON mode) for reliable downstream processing. * **Context Optimization:** Advanced strategies for managing limited context windows (summarization chains, sliding windows, selective context injection) beyond simple truncation. * **Inference Economics:** Understanding the trade\-offs between model size, latency, and cost (e.g., when to route to GPT\-4 vs. a smaller/faster model). **Nice to Have** * Experience with **Elasticsearch** (DSL queries, analyzers). * Knowledge of vector databases and embedding models. * Background in FinTech or familiarity with financial data structures. What You'll Do: * **Agent Architecture:** Design and implement complex agent orchestration logic using **LangGraph**. You will define state management, conditional routing, and error handling within the agent graph. * **Tool Engineering:** Build and optimize the tool layer (function calling) that allows LLMs to interact with internal financial APIs and databases accurately. * **Performance Optimization:** * Reduce end\-to\-end latency through asynchronous processing and streaming (SSE). * Implement semantic caching strategies to minimize API costs and response time. * Optimize token usage without sacrificing answer quality. * **Observability \& Evaluation:** Implement automated evaluation pipelines using **LangSmith**. You will be responsible for setting up regression testing for prompts and agents to measure quality (correctness, faithfulness) before deployment. * **Advanced RAG:** Refine retrieval strategies. Work on hybrid search implementation (Keyword \+ Vector), re\-ranking, and query expansion to feed the most relevant context to the model.