AI Data Engineer - AI Training data

Blueprint Technologies•Redmond, WA

18h•Onsite

About The Position

Blueprint is hiring an AI Data Engineer to develop and maintain Python-based pipelines for creating high-quality AI training datasets. The role processes large-scale structured and unstructured data, transforming raw documents and text into validated, training-ready datasets. It involves building datasets from the ground up, developing scalable data workflows, and managing processing in Azure environments. The engineer will collaborate with data scientists and engineering partners to improve data quality, processing efficiency, and dataset reliability while maintaining data integrity, traceability, and security. The position is based onsite in Seattle, Washington, five days per week. For candidates based in Washington State, the anticipated salary range is $150,000–$160,000 annually. Actual starting pay and job title may vary based on qualifications, experience, skills, and internal equity.

Requirements

At least 5 years of professional experience in data engineering, data pipeline development, or data science with substantial hands-on programming responsibilities.
Strong proficiency in Python, including developing reusable modules, debugging, testing, logging, and error handling.
Demonstrated experience building datasets from scratch, including ingestion, cleaning, transformation, validation, and preparation.
Hands-on experience processing large-scale structured and unstructured datasets using memory-efficient, parallel, or distributed processing techniques.
Practical experience with Azure Blob Storage or Azure Data Lake Storage, including bulk data transfers, storage organization, and access troubleshooting.
Experience with Python data-processing libraries and formats such as pandas, NumPy, JSON/JSONL, CSV, and Parquet.
Working knowledge of SQL, Git, Linux environments, and command-line tools.
Strong troubleshooting, analytical, and problem-solving skills focused on data quality and integrity.
Ability to communicate technical challenges and collaborate effectively with cross-functional engineering and data science teams.

Nice To Haves

Experience developing OCR pipelines and processing scanned documents, multilingual text, or document-layout data.
Experience with Azure Databricks, Azure Machine Learning, Azure Data Factory, or other workflow-orchestration platforms.
Familiarity with Hugging Face Datasets, AI/ML tokenization, near-duplicate detection, and training/evaluation data separation.
Experience with large-scale dataset curation and preparation for AI or machine learning model training.
Knowledge of distributed data-processing frameworks and technologies such as PySpark.
Experience with dataset lineage, reproducibility, containerized environments, and CI/CD practices.
Experience optimizing data pipelines for performance, reliability, scalability, and cost efficiency.

Responsibilities

Design, develop, maintain, and optimize Python-based pipelines for large-scale data ingestion, processing, and transformation.
Build datasets from the ground up by collecting, organizing, cleaning, validating, and preparing structured and unstructured raw 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 and recovery mechanisms.
Develop automated data-quality checks for missing or corrupted files, incomplete processing outputs, duplicate records, formatting issues, and metadata inconsistencies.
Prepare training-ready datasets through filtering, tokenization, text segmentation, and conversion into standardized formats.
Manage bulk data uploads, downloads, and synchronization between local processing environments and Azure storage platforms.
Maintain organized datasets with consistent naming conventions, metadata, version control, and source traceability.
Optimize data-processing performance, memory utilization, storage efficiency, and cloud resource consumption.
Monitor dataset statistics, processing coverage, quality metrics, and exceptions, and generate operational reports.
Collaborate with applied scientists and engineering teams to translate AI training requirements into scalable, repeatable data workflows.
Maintain documented, tested, version-controlled code and follow established data security and governance requirements.