Pythonic AI
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About
Pythonic provides intelligent document processing solutions that read and understand unstructured documents with state-of-the-art precision. Their Precision-first AI highlights data that can’t be processed, simplifying human review. They focus on automating labor-intensive tasks in document workflows involving unstructured data, such as PDFs, in industries like title and escrow and life insurance. Pythonic offers solutions built with domain expertise, SOC2 certified security, and pre-trained models for easy integration.
Platform
Features
• document classification
• data extraction
• document summarization
• intelligent document processing
• document comparison
• deduplication
• document routing
Job Opportunities
SENIOR BACKEND ENGINEER
Pythonic provides intelligent document processing solutions for the title & escrow industry, automating data extraction and processing with high accuracy.
Experience Requirements:
8+ years of relevant industry experience
Responsibilities:
Build backend systems and APIs to provide scalable solutions to our customers
Work with data scientists, business analysts, front-end and data engineers, to define develop and improve products by automating or auto-assisting workflows for customers
Lead the software development lifecycle, from conception to production
Work with cloud security engineers to ensure the product meets security standards (e.g. SOC-2)
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SENIOR ML ENGINEER (SENIOR MACHINE LEARNING ENGINEER)
Pythonic provides intelligent document processing solutions for the title & escrow industry, automating data extraction and processing with high accuracy.
Education Requirements:
Thorough understanding of Machine Learning fundamentals
Experience Requirements:
Experience building deep-net models
Experience with PyTorch or TensorFlow
Excellent problem-solving ability
Responsibilities:
Own the end-to-end solution to automate workflows involving intelligent document processing
Research and develop the right ML/AI models to solve business problems
Implement solutions and guide the deployment
Guide the data engineering team on collecting training data
Guide development of exception handling to provide an end-to-end solution integrating actively labeled data in real-time evolving models to achieve state-of-the-art precision
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