Senior Python AI Engineer (LLM & Multi-Agent Systems) at Seeking Alpha

, , Portugal -

Full Time

Start Date

Immediate

Expiry Date

19 Sep, 26

Salary

0.0

Posted On

21 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Asyncio, FastAPI, LangChain, LangGraph, Pydantic, Elasticsearch, AWS Bedrock, OpenAI API, LangSmith, Generative AI, RAG, Multi-Agent Systems, Prompt Engineering, Vector Databases, Financial Analysis

Industry

technology;Information and Internet

Description

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. 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. 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.

Responsibilities

Design and implement complex multi-agent orchestration logic and tool layers for a high-load financial analysis system. Optimize LLM performance, reduce latency, and implement automated evaluation pipelines using LangSmith.