Data Engineer, AI Backend

Toppan Merrill LLC • New York, NY • 1h ago

Job Description:Data Engineer, AI BackendShape the Future of AI in Capital Markets and Legal DisclosureAt Toppan Merrill, we're building Quinn, a cloud-based platform that helps law firms, investment banks, and public companies draft, review, and file SEC documents, including IPO registration statements, prospectuses, proxy statements, and annual and quarterly reports.We're applying AI to one of the most consequential document workflows in the world, where precision, trust, and accountability matter most. You'll join a small, highly visible team, work directly with senior engineering leadership, and help create AI capabilities used in complex legal and financial disclosure workflows.In this role, you'll build the data and service foundation behind Quinn's in-editor AI: the API the editor team builds against, the pipelines and indexes over SEC filings that power peer-company analysis, and a citation layer that traces every AI answer back to the exact sentence it came from. You'll own both the contract other teams depend on and the systems behind it, in a domain where an untraceable answer isn't acceptable.What You Will Do Publish and own the AI backend's API specification, starting with a working stub, then build and operate the production services behind it, keeping the contract stable and communicating changes clearly to the teams that depend on it. Build a document-@context service that gives the AI the relevant @context from the document an attorney is working on, while respecting that attorney's access permissions. Build ingestion, processing, and indexing pipelines for SEC filings from EDGAR to support peer-company analysis. Create a citation index that connects AI-generated answers to the exact source sentences in the underlying filings. Build services that route requests across multiple AI models while providing logging, monitoring, reliability controls, and visibility into usage and cost. Develop and maintain the pipelines that produce evaluation data used by data scientists to validate AI quality. Establish data-quality, observability, and operational standards for the AI backend. Our stack: Python, FastAPI, Databricks (including Vector Search), Spark, LangChain/LangGraph, Azure OpenAI and Azure AI Foundry, Kubernetes (AKS), and Datadog. How You'll WorkYou'll own the AI backend's data services and pipelines from initial design through production operation. You'll work closely with the data scientists developing and evaluating the AI capabilities, the editor team building the customer-facing AI panel, and the platform engineers who own document access and authorization across Quinn.First 90 Days Own the API specification and stub, and keep the contract stable as the editor team builds against it Stand up the document-@context service in a test environment Get SEC filing ingestion running for an initial set of companiesRequired Qualifications 5+ years of professional data engineering or backend engineering experience in Python. Experience building and operating production data pipelines at scale using Databricks, Apache Spark, or a comparable platform. Experience building and operating production Python services (FastAPI or similar), including APIs other engineering teams depend on: publishing, versioning, and maintaining a stable contract. Experience working with text or document data, search, indexing, or information-retrieval systems. Strong data modeling, pipeline reliability, testing, and observability practices. A track record of delivering against firm milestones with cross-team dependencies. Preferred Qualifications Experience with retrieval-augmented generation (RAG) or vector/hybrid search in production. Experience building citation, provenance, or source-traceability capabilities for search or AI-generated answers. Experience with SEC filings, EDGAR, XBRL, or other complex financial and regulat

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