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. We set high standards, take ownership, and continually challenge ourselves and one another to be better. We work hard, support each other, and take genuine pride in what we deliver for our clients, partners, and teams.

At Blueprint, you’ll work alongside talented people with different experiences, expertise, and perspectives. You’ll have opportunities to take on meaningful challenges, expand your skills, and see the impact of what you build.

Bring your perspective. Raise the standard. Build what matters.

In This Role

You will contribute to the development of AI training datasets by building and maintaining Python-based data processing pipelines. You will work with large-scale structured and unstructured datasets, transforming raw documents and text into high-quality, validated data for AI model training. This role requires hands-on experience creating datasets from the ground up, developing scalable data workflows, and managing data processing in Azure environments. You will 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 data 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 and recovery mechanisms.

Develop automated data quality checks to identify missing or corrupted files, incomplete processing outputs, duplicate records, formatting issues, and metadata inconsistencies.

Prepare high-quality, training-ready datasets through data 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 while generating operational reports.

Collaborate with applied scientists and engineering teams to translate AI training requirements into scalable and repeatable data workflows.

Maintain documented, tested, and version-controlled code while following established 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 responsibilities.

Strong proficiency in Python, including developing reusable modules, debugging, testing, logging, and error handling.

Demonstrated experience building and developing datasets from scratch, including data 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.