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Role Description

This is a highly technical, delivery-focused role at the intersection of AI, data engineering, and quality assurance. You will lead end-to-end coordination of AI data processing workflows, transforming client requirements into structured validation logic and measurable quality checks. Working closely with engineering and operations teams, you’ll ensure datasets meet rigorous standards for accuracy, consistency, and downstream usability. This position requires both project leadership and hands-on technical execution, including scripting, querying, and automation. Operating in a fully remote environment aligned with PST hours, you’ll play a key role in enabling high-quality AI training and evaluation pipelines.

• Own the full lifecycle of AI data deliveries, from intake and validation to final handoff, ensuring datasets meet client specifications and performance metrics.
• Translate client guidelines into automated validation logic using SQL queries, regex rules, and lightweight Python scripts.
• Develop and maintain utilities to calculate quality metrics such as Word Error Rate (WER), Inter-Annotator Agreement (IAA), and other dataset-level indicators.
• Validate data received via cloud storage (e.g., S3 buckets) or direct uploads, ensuring structural accuracy, completeness, and readiness for processing.
• Use command-line tools (Windows CMD) to manage bulk file operations supporting processing and delivery workflows.
• Collaborate with development teams by creating detailed Jira tickets outlining requirements, reproduction steps, and acceptance criteria for improvements or bug fixes.
• Investigate dataset issues through database queries, summarize findings clearly, and recommend corrective actions.
• Advise internal teams on workflow configurations, setup logic, and tradeoffs to improve scalability and maintainability.

Qualifications

• 3+ years of experience in technical project coordination or delivery within data-driven environments (data engineering, analytics, ML operations, QA automation, or platform operations).
• Practical proficiency in Python for scripting and automation of validation workflows.
• Strong SQL skills for querying datasets and implementing automated checks.
• Experience using regex for pattern-based validation and rule enforcement.
• Comfort with command-line environments, particularly Windows CMD, for bulk file management.
• Ability to translate ambiguous client requirements into structured, testable validation criteria.
• Experience collaborating with engineering teams and managing tasks through tools such as Jira.
• Strong analytical thinking and ability to evaluate tradeoffs in technical approaches.
• Excellent written communication skills for documenting processes, findings, and recommendations.
• Availability to work within PST-aligned hours in a remote setting.

Requirements

• Nice to have: Experience with speech or text datasets, familiarity with AWS S3 workflows, or exposure to data labeling and annotation quality frameworks.

Benefits

• Fully remote work model aligned with PST hours.
• Full-time engagement (contract or employment options available).
• Opportunity to work on cutting-edge AI data processing and validation initiatives.
• Exposure to advanced AI systems, including language and multimodal models.
• Collaborative, research-driven technical environment.
• Flexible and growth-oriented culture with hands-on ownership.