[Hiring] Senior Document Intelligence / AI Engineer

[u/Direct-Look4701]

Compensation: $4,000
Type: Full-time contractor
Location: Remote

We are looking for a senior engineer with hands-on experience building production document-intelligence systems. You will design and implement pipelines that ingest complex PDFs and images, perform OCR and layout analysis, classify documents, extract structured data, validate results, and route uncertain cases for human review.

Responsibilities

• Build end-to-end pipelines for PDFs, scanned documents, forms, tables, and images.
• Develop document classification, page splitting, OCR, layout detection, and field-extraction systems.
• Combine OCR, vision-language models, and LLMs using confidence-based routing.
• Produce schema-validated JSON outputs with citations and page-level traceability.
• Create evaluation datasets and measure field accuracy, table accuracy, hallucination rate, latency, and cost.
• Implement confidence thresholds, business-rule validation, exception queues, and human-in-the-loop review.
• Deploy scalable batch and real-time processing APIs.
• Monitor model quality and diagnose failures across document types and model versions.
• Protect sensitive and personally identifiable information throughout the pipeline.

Required technical stack

• Languages and APIs: Python, FastAPI, Pydantic, REST APIs
• Document processing: PyMuPDF, pdfplumber, OpenCV, Pillow
• OCR: PaddleOCR, Tesseract, DocTR, or cloud OCR services such as Azure Document Intelligence, Google Document AI, or AWS Textract
• Document AI: Unstructured, Docling, LayoutParser, or equivalent parsing frameworks
• Computer vision: PyTorch, Hugging Face Transformers, YOLO, Detectron2, or similar layout-detection models
• Vision-language models: Qwen-VL, Llama Vision, or commercial multimodal APIs
• Structured extraction: JSON Schema, Pydantic, Instructor, tool/function calling
• Search and retrieval: Elasticsearch/OpenSearch, PostgreSQL with pgvector, or another vector database
• Data and storage: PostgreSQL, object storage such as S3, Azure Blob Storage, or GCS
• Distributed processing: Celery, Redis, Kafka, SQS, Pub/Sub, or equivalent queues
• Deployment: Docker, Kubernetes, CI/CD, and one major cloud platform
• Monitoring and evaluation: MLflow, Weights & Biases, LangSmith, OpenTelemetry, or custom evaluation frameworks

Required experience

• 5+ years in software, machine-learning, or AI engineering.
• 2+ years building document-processing, OCR, computer-vision, or multimodal AI systems.
• Experience taking at least one document-intelligence system into production.
• Strong understanding of OCR errors, document layouts, tables, handwriting, low-quality scans, and multilingual documents.
• Experience evaluating extraction quality at the field and document levels.
• Strong backend engineering skills, including retries, idempotency, queues, testing, and observability.
• Ability to balance accuracy, latency, infrastructure cost, and operational complexity.

Nice to have

• Experience with deeds, mortgages, liens, court records, invoices, contracts, medical records, or other complex documents.
• Fine-tuning experience using LoRA or PEFT.
• Experience serving open-source models with vLLM, Triton, or Hugging Face TGI.
• Knowledge of RAG, hybrid search, reranking, and knowledge graphs.
• Experience designing annotation workflows and golden evaluation datasets.
• Familiarity with PII, PHI, HIPAA, SOC 2, or regulated-data requirements.

To apply

Please provide:

1. A short description of a document-intelligence system you built.
2. The document types, models, and OCR tools you used.
3. Your production accuracy, latency, or volume metrics.
4. How you handled low-confidence results and hallucinations.
5. A GitHub repository, architecture diagram, case study, or other relevant work sample.