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, 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. - 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.