Applied AI Engineer - San Francisco

Only for registered members San Francisco, United States

1 day ago

Default job background
$90,000 - $155,000 (USD) per year *
* This salary range is an estimation made by beBee
About the Role We are looking for an Applied AI Engineer to build intelligent trading agents and RAG-powered research systems for financial markets. · You will combine LLMs, real-time market data, and autonomous agent architectures to create systems that analyze, reason, and act ...
Job description
About the Role

We are looking for an Applied AI Engineer to build intelligent trading agents and RAG-powered research systems for financial markets.

You will combine

LLMs, real-time market data, and autonomous agent architectures to create systems that analyze, reason, and act on financial information — from earnings reports to live order books.

Responsibilities

  • Trading Agents: Design and build autonomous AI agents that monitor markets, interpret signals, execute trades, and manage risk — with humanintheloop
controls and safety guardrails

  • Financial RAG Systems: Build RAG pipelines over SEC filings, earnings transcripts, research reports, news, and alternative data — enabling agents to
ground decisions in up-to-date financial knowledge

  • Strategy Development: Collaborate with quants and traders to encode trading logic, risk rules, and portfolio constraints into agent workflows
  • Evaluation & Backtesting: Build evaluation frameworks to measure agent performance — retrieval accuracy on financial documents, signal quality, PnL
attribution, and risk-adjusted returns

  • Observability & Safety: Implement logging, position limits, kill switches, anomaly detection, and audit trails — production trading systems have zero
tolerance for silent failures

  • Infrastructure: Deploy lowlatency AI systems on cloud infrastructure with reliable message queues, state management, and failover
Requirements

  • 3+ years in software engineering, with 1+ year building LLMpowered applications
  • Handson experience building RAG systems — chunking, embeddings, hybrid retrieval, reranking
  • Experience building AI agents with tool calling, multistep reasoning, and stateful workflows
  • Understanding of financial markets — equities, options, futures, or crypto; you know what a limit order, Greeks, or Sharpe ratio are
  • Strong Python skills; experience with async programming and eventdriven architectures
  • Experience with vector databases (Qdrant, Pinecone) and/or graph databases (Neo4j)
  • Systematic thinker — you build for correctness and reliability, not just demos


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