AI Engineer

AI Engineer
Remote
September 10, 2026

Job Description

REQUIREMENTS

  • Python expertise. Deep proficiency with async Python, type annotations, and data manipulation; comfortable in a modern Python 3.12+ fully-typed codebase.
  • LLMs and multimodal vision. Hands-on production experience with frontier LLM APIs (OpenAI, Anthropic, or equivalent) using vision capabilities to read complex multi-page financial tables.
  • Structured AI outputs. Advanced use of Pydantic, Instructor, or native structured-output APIs to force LLMs into validated, system-ready JSON – not hoping the model behaves, enforcing it.
  • Document intelligence and OCR. Proven experience with enterprise document parsing pipelines; prior Azure Document Intelligence or Azure Content Understanding exposure strongly preferred.
  • Relational data discipline. Solid PostgreSQL data modeling and migration practices (SQLAlchemy/Alembic or equivalent); reconciliation logic lives in the database as much as in the pipeline.
  • Security and data-handling mindset. Familiarity with financial data obligations, zero-data-retention LLM agreements, regional endpoints, and why redaction must precede the API boundary.

Nice to have

  • Domain experience in accounting, fintech, legal, or audit-compliance contexts
  • PDF internals: manipulating documents at the object or glyph level (pikepdf, pdfminer.six, pypdfium2, or similar)
  • Orchestration with distributed task queues – our pipelines run as taskiq workers over RabbitMQ and Redis
  • PII pipeline experience with privacy frameworks or custom NLP/regex hybrid engines
  • Observability instrumentation with OpenTelemetry shipped to Azure Monitor

RESPONSIBILITES

  • Own the AI extraction layer. Design and maintain high-accuracy pipelines that parse tabular, structured, and unstructured financial data from scanned and digital PDFs – trust statements, payroll documents, vendor reports – using Azure Document Intelligence and frontier LLM vision APIs.
  • Build and harden PII redaction. Extend our in-house PDF redaction toolkit so that SSNs, EINs, account numbers, and names are masked programmatically before data crosses any external API boundary.
  • Design reconciliation workflows. Implement the cross-referencing and matching logic – deterministic where Python or SQL wins, agentic only where justified – that reconciles data extracted from disparate financial sources.
  • Deliver clean, documented REST APIs. Wrap AI workflows into secure FastAPI services with strict Pydantic-validated inputs and outputs that our core development team consumes with confidence.
  • Enforce guardrails and run evals. Detect and reject low-confidence extractions, build automated regression suites against real documents, and maintain measurable accuracy benchmarks over time.
  • Operate reliably on Azure. Deploy and monitor microservices on AKS, instrument with OpenTelemetry traces and metrics to Azure Monitor, and keep services auditable and production-grade.

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