AI Engineer

Responsibilities:
Data Pipeline Development: Build and maintain pipelines that ingest, clean, and structure financial data from multiple sources - market data, filings, earnings transcripts, macro feeds - handling the inconsistencies, latency differences, and structural complexity that come with real-world financial data at scale.
RAG and Knowledge Graph Development: Build and maintain retrieval systems and knowledge graphs that give agents accurate, structured access to financial data. Continuously iterate on retrieval quality - improving how data is chunked, indexed, and surfaced - based on agent performance and input from investment analysts.
Agent System Development: Build and implement multi-agent systems capable of carrying out complex, multi-step research workflows - coordinating specialist agents across retrieval, reasoning, and synthesis and managing state and context across long-running runs. Work within the broader system architecture to ensure agents access tools and data correctly and that outputs are structured for downstream use.
Evaluation and Iteration: Instrument agent runs to capture structured output that supports systematic review and failure analysis. Identify failure patterns - retrieval errors, reasoning gaps, data quality issues - and iterate continuously. Work closely with investment analysts to understand where outputs are falling short, and incorporate their feedback into retrieval systems, tooling, and agent behavior on an ongoing basis.
Frontend and API Integration: Connect agent systems and data infrastructure to front-end applications via clean, reliable APIs. Wrap data sources, financial models, and external services into versioned tools that agents and downstream systems can depend on.
Requirements:
Experience: 3-6 years in data engineering, ML infrastructure, or backend systems, with recent hands-on experience building LLM agent systems in the context of financial or deep research applications. Experience working with equity research data - filings, earnings transcripts, pricing feeds, financial statements, and consensus estimates - is a strong plus.
Education: Master's or PhD in Computer Science, Engineering, or a related quantitative field preferred. Strong portfolios of shipped projects or open-source contributions are equally welcome.
Agent Systems: Hands-on experience with agentic frameworks (LangGraph, Google ADK, or equivalent). Practical understanding of prompt chaining, tool use, memory, and multi-agent orchestration.
RAG and Knowledge Graphs: Experience building and iterating on retrieval-augmented generation systems and knowledge graphs. Able to diagnose how retrieval quality affects agent output.
Evaluation: Experience building evaluation infrastructure for LLM systems - structured failure analysis and tight iteration cycles with domain experts.
Engineering: Strong in Python. Comfortable with SQL, ETL pipelines, and building APIs. Familiarity with AWS, Azure, or GCP is a plus.

Experience
3-6 yrs