Artificial Intelligence Engineer

Applied AI Engineer — Search & IntegrationNew York | All levels considered (new grad through senior)About the Company: Our client is an AI-native company building the intelligence layer for global public markets. Their platform is used daily by 70+ financial institutions across the US, Europe, and Asia, spanning sell-side research teams and buy-side asset managers and hedge funds collectively managing over $5 trillion in assets. They recently closed a $22M Series A, backed by a global syndicate of strategic financial institutions and venture investors.The Opportunity: The Applied AI Engineering (Search & Integration) team cuts across the whole product, from APIs through the AI services (search, chat, the autonomous research agent) down to the data layer beneath them. This role owns the document pipeline end to end, from ingestion through parsing, chunking, extraction, indexing, and search, and is central to how the company's AI research agent finds and reasons over institutional research material.What You'll Do:Own the document pipeline end to end (connectors, parsing, chunking, extraction, indexing, search), with "the data layer actually serves the research agent" as the definition of doneDesign search for agents, not just people, shaping the identification layer (metadata, filters, knowledge-graph evolution) around how the research agent plans multi-step workTurn the core product into APIs, hardening the internal skill/tool system for external systems to consumeBuild customer-driven R&D on top of the core data layer, such as quantitative screening and portfolio-analysis featuresGo down the stack when needed, including integrations, connectors, backend services, and the infrastructure they run onOwn cloud infrastructure decisions directly, with changes shipped as pull requests against Terraform/Helm/ArgoCDMake improvements measurable through extraction-quality evals, search precision/recall against labeled query sets, and field-level parity checksWhat They're Looking For:Buy-Side Background: Engineering experience within, or built for, an asset manager or hedge fund, with a real understanding of how institutional research actually gets consumedSearch & Retrieval: Hands-on experience building or operating search, retrieval, or document-processing systems in production. Strong candidates can speak to the nature of the document corpus they worked with, how it shaped their search design, and how they measured whether search actually performed, and whyEngineering Stack: Python (FastAPI, Pydantic, SQLModel/SQLAlchemy) and comfort with TypeScript; exposure to Kubernetes, OpenSearch, and Postgres is a plusAI/LLM Systems: Experience with multi-model LLM extraction or evaluation pipelines, including retry/fallback design and structured outputsFull-Stack Comfort: Willingness to go down the stack, from infrastructure through to the AI services layerAdaptability: Comfortable operating in a fast-moving, technically deep startup environmentLogistics:Location: New York (Onsite 5 days per week)Stage: Series A ($22M, closed July 2026)