3 months ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 21 hours agoStatus: Live

Job Summary

Build and scale Ask Seeking Alpha, a high-load financial analysis system powered by large language models. You’ll design multi-agent orchestration with LangGraph, engineer tool/function calling to integrate internal APIs and databases, and improve reliability for non-deterministic outputs. Own performance optimization (async processing, streaming, semantic caching), advanced RAG (hybrid search, re-ranking, query expansion), and automated evaluation with LangSmith before production deployment.

Location: Ukraine

Workplace: Remote

Employment Type: Contract

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and implement multi-agent orchestration using LangGraph, including state management, conditional routing, and error handling.
•Build and optimize the tool layer (function calling) so LLMs can accurately interact with internal financial APIs and databases.
•Reduce end-to-end latency using asynchronous processing and streaming (SSE).
•Implement semantic caching and optimize token usage to improve cost and response time without reducing answer quality.
•Develop automated evaluation pipelines with LangSmith and improve retrieval for advanced RAG (hybrid search, re-ranking, query expansion).

Key Requirements

•Strong modern Python skills with mandatory async programming (asyncio) and experience building non-blocking I/O pipelines.
•Experience with FastAPI and Pydantic (v2).
•Production experience with LangChain and hands-on knowledge of LangGraph (or similar state-machine agent frameworks).
•Deep LLM expertise, including handling non-determinism (e.g., self-correction, CoT/ReAct) and forcing structured outputs to strict schemas (e.g., Pydantic/JSON mode).
•Ability to optimize context usage for LLMs and understand inference trade-offs between latency and cost.

Experience:FinTechLLM

Skills:Reliability focusProblem-solvingAttention to detail

Tech Stack:PythonAsyncioFastAPILangChainLangGraphPydanticElasticsearchAWS BedrockOpenAI APILangSmithSSECoTReActJSON mode

Company Brief

Seeking Alpha

Provides financial news, market analysis, and investment research for retail investors and professionals. The platform features articles, earnings coverage, stock ideas, portfolio tools, and community discussions focused on public markets.

Industry: News, Journalism & Broadcasting

Company Size: Large (251 to 1,000 employees)

Growth: Established Company

Headquarters: New York City, United States

Founded: 2004

Website LinkedIn

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Seeking Alpha

3 months ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 21 hours agoStatus: Live

Job Summary

Location: Ukraine

Workplace: Remote

Employment Type: Contract

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and implement multi-agent orchestration using LangGraph, including state management, conditional routing, and error handling.
•Build and optimize the tool layer (function calling) so LLMs can accurately interact with internal financial APIs and databases.
•Reduce end-to-end latency using asynchronous processing and streaming (SSE).
•Implement semantic caching and optimize token usage to improve cost and response time without reducing answer quality.
•Develop automated evaluation pipelines with LangSmith and improve retrieval for advanced RAG (hybrid search, re-ranking, query expansion).

Key Requirements

•Strong modern Python skills with mandatory async programming (asyncio) and experience building non-blocking I/O pipelines.
•Experience with FastAPI and Pydantic (v2).
•Production experience with LangChain and hands-on knowledge of LangGraph (or similar state-machine agent frameworks).
•Deep LLM expertise, including handling non-determinism (e.g., self-correction, CoT/ReAct) and forcing structured outputs to strict schemas (e.g., Pydantic/JSON mode).
•Ability to optimize context usage for LLMs and understand inference trade-offs between latency and cost.

Experience:FinTechLLM

Skills:Reliability focusProblem-solvingAttention to detail

Tech Stack:PythonAsyncioFastAPILangChainLangGraphPydanticElasticsearchAWS BedrockOpenAI APILangSmithSSECoTReActJSON mode

Company Brief

Seeking Alpha

Industry: News, Journalism & Broadcasting

Company Size: Large (251 to 1,000 employees)

Growth: Established Company

Headquarters: New York City, United States

Founded: 2004

Website LinkedIn