Senior Automation ML Engineer

Role: Senior

• Owns ongoing model performance and enhancement for 1 or more Dodge entities / domains
• Deeply analyzes Dodge datasets in order to suggest best solutions for data management and enrichment using AI / ML
• Design, develop, and test machine learning models to automate data enrichment, classification, and validation processes.
• Develop Python-based automation scripts and microservices to reduce manual effort in project matching, contact discovery, and quality checks.
• Implement NLP models for entity recognition (e.g., identifying architects, GCs, and project roles from unstructured text, pdf documents).
• Implement OCR, NLP, and layout recognition techniques to extract project metadata, deadlines, contacts, and technical requirements.
• Build Python-based scripts and microservices to classify documents by type and extract structured fields (e.g., bid dates, scope of work, etc).
• Build pipelines that integrate scraped project data with external APIs (ZoomInfo, LinkedIn, etc.) to enrich company and contact information.
• Collaborate with data engineers to ensure ML pipelines integrate seamlessly with existing data warehouses.
• Partner with data specialists to design feedback loops that validate and improve model outputs.

Required Qualifications:

• 5+ years of experience in Machine Learning and automation engineering.
• Proficiency in Python with hands-on experience using libraries such as scikit-learn, spaCy, TensorFlow, or PyTorch.
• Hands-on experience with

  OCR frameworks

  (Tesseract, PaddleOCR, AWS Textract, Google Document AI).
• Familiarity with

  document layout analysis

  (LayoutLM, Donut, DocTR, etc.).
• Strong knowledge of

  regex, rules-based parsing, and entity extraction

  techniques.
• Strong knowledge of data pipelines and ETL frameworks.
• Experience deploying ML models into production, monitoring performance, and maintaining pipelines.
• Solid understanding of relational databases and SQL; experience with large-scale warehouses (e.g., Redshift, Snowflake).
• Demonstrated experience automating repetitive tasks with Python, APIs, and workflow orchestration.
• Strong problem-solving skills with the ability to translate business use cases (project/contact enrichment, validation) into ML/automation solutions.

  Preferred Qualifications:

• Experience with Named Entity Recognition (NER) and text classification models for parsing unstructured construction/project documents.
• Familiarity with AWS analytics/ML services (SageMaker, Comprehend, Lambda, Step Functions).
• Exposure to CI/CD pipelines and MLOps tools (MLflow, Git, Docker, Kubernetes).
• Prior experience working with sales intelligence data (contacts, companies, lead enrichment).
• Experience in Agile delivery environments using Jira or Confluence.

Mode of Work: Hybrid