AI Quality Automation Engineer , DTG

Pure Storage

Location not specified

Full Time

Posted August 5, 2026

$76k - $110k· Est.

Not Specified

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Job Description

And because we understand the value of bringing your full and best self to work, we offer a variety of perks to manage a healthy balance, including flexible time off, wellness resources, and company-sponsored team events.

Key Responsibilities

As an AI Quality Automation Engineer on the Master Data Management (MDM) team, you will drive data reliability by building intelligent, automated testing systems for complex data pipelines. Working alongside data engineers, software developers, and enterprise QA teams, you will bridge data infrastructure and high-velocity software delivery. Your mission is to deploy scalable, AI-driven test frameworks that ensure precision across enterprise data assets, directly elevating global data quality and scaling our core data engine.
AI-Powered Quality Frameworks: Design and execute automated test suites and AI evaluation frameworks to detect data anomalies early, validate complex pipeline logic, and eliminate manual quality overhead across enterprise data infrastructure.
Expand the E2E AI Testing Platform: Scale and optimize an end-to-end testing platform using generative AI models and agentic workflows to increase test coverage and accelerate release velocity for core MDM applications.
Automate Continuous Integration Pipelines: Build resilient CI/CD deployment workflows using GitHub Actions to automate MDM software delivery, ensuring high stability and continuous release velocity.
Drive Data Precision & Governance: Partner with cross-functional engineering teams to establish automated quality gates, author comprehensive test plans, and define data precision metrics that safeguard enterprise data integrity.
Enhance Operational Telemetry & Observability: Integrate automated telemetry and real-time monitoring into existing CI/CD pipelines to enable continuous observability, swift error isolation, and peak platform performance.
Engineer Resilient Quality Sub-Systems: Own the technical design and execution of quality engineering sub-systems, translating intricate data structures into repeatable, automated validation mechanisms.

Requirements

Experience: 3+ years of professional experience in quality software engineering, test automation development, or data platform testing.
AI & LLM Integration: Experience with Prompt Engineering, fine-tuning test strategies for Large Language Models (LLMs), and implementing AI-driven test automation frameworks.
Software Development: Proficiency in Python or modern object-oriented programming to engineer modular test suites, custom API automation tools, and validation pipelines.
DevOps & CI/CD Pipelines: Hands-on experience constructing and managing CI/CD deployment pipelines using GitHub Actions and modern continuous integration tools.
Data Platform Validation: Demonstrated skill in validating complex data pipelines, automated regression testing suites
Problem-Solving & Collaboration: Ability to independently resolve complex technical problems, perform root-cause analysis, and collaborate across functional engineering teams.
Location: We are primarily an in-office environment and therefore, you will be expected to work from the Prague, Czech Republic office in compliance with Pure’s policies, unless you are on PTO, or work travel, or other approved leave.
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