Machine Learning Engineer

Location: Remote (occasional onsite possible)

Team: 6–8 people (Lead, Senior, Mid-level Developers)

Focus: Production-grade AI for content extraction and classification (text & image)

About the RoleWe’re building a brand-new AI team from scratch. The mission: to design, build, and deploy real-world AI/ML models that extract and classify information from text and images. The focus is on solving actual business problems with production-ready AI – not just prototypes.

Your Responsibilities

• Build and deploy production-grade ML/AI models for document content extraction and classification (text + image)

• Work with OCR technologies and NLP methods on unstructured and semi-structured documents

• Collaborate with product managers and stakeholders to define use cases and deliver working AI solutions

• Build and maintain Python-based components and pipelines, including model deployment

• Define and clearly explain practical application scenarios for your models

• Ensure safe, secure, and resilient model deployment in production environments

Must-Have Requirements

• Minimum 2 years of hands-on experience deploying ML/AI models into production environments

• Strong experience with OCR, including open-source tools (e.g. Tesseract, EasyOCR)

• Solid knowledge of at least one major ML/NLP/CV framework: TensorFlow, PyTorch, Hugging Face, SpaCy, OpenCV

• Experience with open-source machine learning tools and classical ML methods (not only GenAI)

• Practical experience in Regex, NLP, and text/image classification

• Ability to articulate how a model works in a real-world business scenario

• Experience with AI agents or systems that orchestrate multiple AI components

• Strong coding skills in Python with a focus on secure, production-ready development

Nice to Have

• Experience with Document AI or Intelligent Document Processing (IDP)

• Familiarity with PostgreSQL

• Experience working with Microsoft Azure services (AKS, DevOps, CLI, VMs)

• Understanding of CI/CD pipelines and frequent deployment cycles