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

Seeking Alpha; Lisboa; Híbrido; EUR 60 000 - 80 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Flexible work environment

Remote work options

Variety of perks

Resumo da oferta

Seeking Alpha is looking for a Senior Python AI Engineer to develop 'Ask Seeking Alpha', a high-load financial analysis system. The role involves complex agent orchestration using LangGraph and optimizing interactions with generative AI models.

Candidates should have strong proficiency in Python, experience with asynchronous programming, and be familiar with FastAPI and LangChain. The position offers remote work options and a balanced environment.

Qualificações

• Strong proficiency in modern Python is mandatory.
• Experience with FastAPI and Pydantic (v2) is required.
• Hands-on experience or deep understanding of state-machine based frameworks.

Responsabilidades

• Design and implement complex agent orchestration logic using LangGraph.
• Build and optimize the tool layer for LLMs to interact with financial APIs.
• Reduce end-to-end latency through asynchronous processing.

Conhecimentos

Strong proficiency in modern Python

Deep understanding of asynchronous programming (asyncio)

Experience with FastAPI

Production experience with LangChain

Experience with Elasticsearch

Ferramentas

LangGraph

Pydantic

OpenAI API

AWS Bedrock

Descrição da oferta de emprego

Overview

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. We’re dedicated to creating a workplace where our team thrives, with a flexible, balanced environment, remote work options, and a variety of perks that make a real difference.

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.