Applied AI Engineer - San Francisco
1 day ago

Job description
About the RoleWe are looking for an Applied AI Engineer to build intelligent trading agents and RAG-powered research systems for financial markets.
You will combineLLMs, 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
- Financial RAG Systems: Build RAG pipelines over SEC filings, earnings transcripts, research reports, news, and alternative data — enabling agents to
- 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
- Observability & Safety: Implement logging, position limits, kill switches, anomaly detection, and audit trails — production trading systems have zero
- Infrastructure: Deploy lowlatency AI systems on cloud infrastructure with reliable message queues, state management, and failover
- 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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