AI Engineer: Document Intelligence & Machine Learning

EarlySafe works with complex financial and lending information including financial statements, bank statements, GST information, property documents, legal reports, valuation reports, verification reports, scanned PDFs, tables and images.

We are looking for an AI/ML Engineer who has built and deployed ML or Document AI systems in production and can transform unstructured information into reliable, structured evidence.

What You Will Do

• Build production Document AI pipelines
• Develop OCR, layout understanding, table extraction and document classification
• Use VLMs and multimodal models for document understanding
• Extract entities, values and evidence from unstructured information
• Develop ML models for classification, anomaly detection, similarity and pattern recognition
• Reconcile information across documents and data sources
• Build confidence scoring, validation and exception handling
• Develop training, inference and evaluation pipelines
• Monitor model accuracy, drift and production failures
• Build scalable APIs and services consumed by EarlySafe applications

Production Experience Required

You should have taken ML/AI models beyond notebooks and POCs into real production environments.

You should understand:

Training/Inference Pipelines • Model Serving • APIs • Docker • Cloud Deployment • CI/CD • Model Versioning • Monitoring • Data/Model Drift • Batch Processing • Scalability • Failure Handling

Experience designing for large documents, high-volume processing and asynchronous workloads is particularly valuable.

Skills We Value

Core:
Python • Machine Learning • Document AI • VLMs • NLP/Information Extraction • OCR/Layout Understanding • Production ML

Useful:
PyTorch/TensorFlow • Hugging Face • Scikit-learn • OpenCV • XGBoost/LightGBM • FastAPI • MLflow • Spark/Databricks • Docker/Kubernetes • AWS/Azure/GCP

What We Are Looking For

We want engineers who care about accuracy, confidence, provenance, reconciliation and reproducibility.

The question isnt simply whether the model returned an answer. It is whether that answer can be trusted at enterprise scale.

Experience with financial documents, banking, lending, fintech or Intelligent Document Processing is particularly valuable.