AI Data Engineer - AI Training data
Bpcs · Redmond, WA
About this role
**About Blueprint** Blueprint is a technology solutions firm headquartered in Bellevue, Washington, with teams across the United States. We help organizations turn complex challenges into meaningful outcomes by connecting strategy and execution across AI, cloud, data, product development, and emerging technology. Our culture is built by people who care deeply about doing exceptional work—setting high standards, taking ownership, and continually challenging ourselves and one another to be better. **In This Role** You will contribute to the development of AI training datasets by building and maintaining Python-based data processing pipelines. You’ll work with large-scale structured and unstructured datasets, transforming raw documents and text into high-quality, validated data for AI model training. You’ll collaborate with data scientists and engineering partners to improve data quality, processing efficiency, and dataset reliability—while ensuring data integrity, traceability, and security. **Responsibilities** - Design, develop, maintain, and optimize Python-based data pipelines for large-scale ingestion, processing, and transformation - Build datasets from the ground up by collecting, organizing, cleaning, validating, and preparing raw structured and unstructured data - Develop workflows for OCR processing, text extraction, normalization, filtering, segmentation, and deduplication - Execute and monitor large-scale batch processing jobs; troubleshoot failures and implement automated retry/recovery - Develop automated data quality checks (missing/corrupted files, incomplete outputs, duplicates, formatting issues, metadata inconsistencies) - Prepare training-ready datasets via filtering, tokenization, text segmentation, and conversion into standardized formats - Manage bulk data uploads/downloads and synchronization between local processing environments and Azure storage - Maintain organized datasets with consistent naming conventions, metadata, version control, and source traceability - Optimize performance, memory utilization, storage efficiency, and cloud resource consumption - Monitor dataset statistics, processing coverage, quality metrics, and exceptions; generate operational reports - Collaborate with applied scientists and engineering teams to translate AI training requirements into scalable, repeatable workflows - Maintain documented, tested, version-controlled code while following data security and governance requirements **Qualifications** - 5+ years of professional experience in data engineering, data pipeline development, or data science with substantial hands-on programming - Strong Python proficiency (reusable modules, debugging, testing, logging, error handling) - Experience building datasets from scratch (ingestion, cleaning, transformation, validation, preparation) - Hands-on experience processing large-scale structured/unstructured datasets using memory-efficient, parallel, or distributed techniques - Practical experience with Azure Blob Storage or Azure Data Lake Storage (bulk transfers, organization, access troubleshooting) - Experience with Python data-processing libraries and formats (pandas, NumPy, JSON/JSONL, CSV, Parquet) - Working knowledge of SQL, Git, Linux, and command-line tools - Strong troubleshooting/analytical/problem-solving skills focused on data quality and integrity - Ability to communicate technical challenges and collaborate cross-functionally **Preferred Qualifications** - Experience develop
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