AI Developer

Role Description

We are looking for an AI Developer who can build intelligent document processing pipelines - primarily focused on extracting structured and unstructured data from PDFs, with principles that extend to image-based inputs. You will design and ship OCR-powered solutions that turn raw documents (contracts, forms, reports) into clean, queryable data, and integrate LLM reasoning layers on top using OpenAI and Anthropic APIs. This is a hands-on engineering role. You will own the full pipeline: ingestion, OCR, parsing, prompt engineering, and API delivery via FastAPI.

What You'll Do:

• Document Intelligence & OCR:
  • Design and build end-to-end PDF and document extraction pipelines (flat text and structured output).
  • Select and implement the right OCR strategy per document type - native PDF text layer, layout-aware parsing, or image-based OCR.
  • Parse complex layouts: multi-column text, tables, headers/footers, embedded figures, form fields.
  • Output clean structured JSON or relational data from raw document inputs.
• Backend API Development:
  • Build and maintain FastAPI services that expose document processing capabilities.
  • Design async endpoints for large document batches; handle timeouts, retries, and partial failures gracefully.
  • Write clean, testable Python code; follow REST best practices.
  • Integrate with storage layers (S3 / GCS), queues (Google Pub/Sub), and downstream systems as needed.

Qualifications

• Hands-on experience building OCR or document extraction pipelines in production.
• Strong Python skills - clean, maintainable code with proper error handling.
• Practical experience with FastAPI (routing, dependency injection, async, middleware).
• Prompt engineering experience with OpenAI or Anthropic APIs - not just calling the API, but designing reliable extraction chains.
• Familiarity with PDF internals: text layers, bounding boxes, embedded fonts, page structure.

Requirements

• OCR & Document Processing Skills:
  • Native PDF text extraction: pdfplumber, PyMuPDF, pdfminer - fast, accurate when the text layer exists; must detect and fall back when it doesn't.
  • Layout-aware parsing: Preserve reading order across columns, tables, and mixed content blocks.
  • Image-based OCR: Tesseract, EasyOCR, or cloud OCR (AWS Textract, Google Document AI, Azure Form Recognizer) for scanned inputs.
  • Table extraction: Structured output from tabular data - row/column alignment, merged cells, nested tables.
  • Output formats: Flat text, structured JSON, markdown - output type driven by downstream use case.
• Nice to Have:
  • Experience with vision-language models (GPT-4V, Claude 3 vision) for image-heavy documents.
  • Comfortable with AI-driven development (fully Developer-in-the-loop).
  • Cloud OCR: AWS Textract, Google Document AI, or Azure Form Recognizer.
  • LangChain, LlamaIndex, or similar orchestration frameworks.
  • Vector search / RAG pipelines for document Q&A.
  • Docker, basic CI/CD, and cloud deployment (AWS / GCP / Azure).
  • Experience with agentic workflows (tool use, multi-step LLM chains).
• LLM Integration & Prompt Engineering:
  • Write, test, and iterate prompts for OpenAI (GPT-4o, GPT-4 Turbo) and Anthropic (Claude) models.
  • Apply prompt engineering techniques: chain-of-thought, few-shot, structured output forcing, tool use/function calling.
  • Build extraction agents that combine OCR output with LLM reasoning for ambiguous or complex documents.
  • Evaluate and benchmark prompt strategies; document what works and why.

Benefits

• You care about output quality - you're not happy until the extraction is clean and reliable.
• You test your prompts like you test your code - systematically, with real data.
• You know when to use an LLM and when a regex is the better tool.
• You can communicate tradeoffs clearly to non-technical stakeholders.
• You're comfortable in a fast-moving, remote-first environment.