AI Developer

Role Overview: 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.

Must-Have Requirements: Core — these are non-negotiable. 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 OCR & Document Processing Skills: We work primarily with PDFs, but the underlying principles apply equally to scanned images. You should know when and how to apply each approach: Approach | When to Use / What to Know Native PDF text extraction | pdfplumber, Py.

MuPDF, 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. Lang. Chain, Llama. Index, 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. Agents & Orchestration 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.

You'll Thrive Here If: 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.