InStore.ai
Website:
instore.ai
Job details:
Company
InStore.ai helps organizations measure and improve in-person customer experiences by transforming voice conversations between frontline employees and shoppers into actionable insights. By focusing on cashier engagement and category-level intelligence, the company enables retailers and brands to better understand customer behavior at the point of sale. These real-time insights uncover opportunities to increase revenue, optimize operations, and reduce costs. InStore.ai works closely with clients to integrate its solutions into existing workflows, allowing teams to quickly act on findings and improve performance. The environment is collaborative, data-driven, and focused on innovation in retail and customer experience analytics.
The Role
The AI Automation QA Engineer is a full-time remote role responsible for ensuring the quality and reliability of InStore.ai’s analytics and AI-driven platforms. Each day, this role designs, executes, and maintains test plans and test cases for new and existing features, with a focus on both functional and non-functional requirements. The engineer is also involved in validating the quality, reliability, and performance of data ingestion workflows that support AI and machine learning systems. Responsibilities include performing manual and automated testing, testing structured and unstructured data pipelines, ensuring data accuracy, validating transformations, identifying ingestion failures, analyzing test results, documenting defects, and working with the team to prioritize and resolve issues. The role also contributes to improving QA processes, creating testing standards, and helping implement continuous testing practices within the development lifecycle.
The ideal candidate has experience testing APIs, data validation logic, cloud data platforms, and AI/ML data workflows. They should be comfortable working with engineers, data scientists, and product teams to understand business and technical requirements, identify edge cases, and to ensure that ingested data is complete, accurate, traceable, and ready for downstream AI use cases.
Key Responsibilities
Testing Tasks
- Design, execute, and maintain test plans for AI data ingestion pipelines.
- Validate data extraction, transformation, normalization, enrichment, and loading processes.
- Test ingestion workflows for structured, semi-structured, and unstructured data sources.
- Verify data quality dimensions including completeness, accuracy, consistency, freshness, duplication, and schema compliance.
- Identify, document, and track pipeline defects, data anomalies, and regression issues.
- Collaborate with data engineers, ML engineers, and product teams to define acceptance criteria.
- Monitor pipeline performance, latency, throughput, and failure recovery.
Test Automation and Coverage
- Design, build, and maintain end-to-end test suites using Python and/or TypeScript.
- Contribute to QA automation strategy, CI/CD testing, and release validation.
Frameworks and Tooling
- Develop and maintain automated tests written in Python or TypeScript.
- Comprehensive test execution and failure analysis with generated HTML reports for easier debugging and root-cause identification.
Code Quality and Engineering Standards
- Produce clean, maintainable test code that reflects sound software design, including the SOLID principles.
- Surface regressions early and help establish testing standards and best practices across the team.
Collaboration and Communication
- A meticulous, detail-oriented approach and a genuine commitment to building things correctly.
- Ability to collaborate effectively across a fully remote, cross-functional team, partnering closely with AI and engineering teams to align testing efforts with product priorities.
- Strong written and verbal communication skills, with the ability to clearly articulate findings, risks, and recommendations to both technical and non-technical stakeholders.
- Ability to work independently, adapt quickly, and thrive in a fast-paced, evolving environment.
What We’re Looking For
Experience and Skills
- At least four years of professional, commercial experience in QA, data QA, data engineering QA, or test automation.
- Demonstrated proficiency writing end-to-end tests, with hands-on use of PyTest.
- Strong Elastic Search skills for data validation and troubleshooting.
- Experience with Python for test automation or data validation.
- Familiarity with APIs, JSON, CSV, relational databases, Elastic Search, and cloud storage.
- Experience using issue tracking and test management tools such as Jira, TestRail, or similar.
- Ability to analyze logs, troubleshoot failures, and communicate defects clearly.
- Strong attention to detail and comfort working with complex datasets.
- A solid grounding in software engineering fundamentals, including the SOLID design principles.
- A Bachelor's degree in Computer Science or a closely related technical discipline.
- Experience with AI/ML data pipelines, LLM applications, RAG systems, embeddings, or vector databases.
- Proven ability to collaborate seamlessly with a remote, international workforce, navigating various time zones and diverse cultures with professional communication and coordination.
Preference to Candidates With:
- Advanced English proficiency (C1 or C2).
- A track record of independently building or bootstrapping personal and side projects (portfolio or repository links welcome).
- Exposure to full-stack development across React, Node.js, and Python.
- Familiarity with at least one major cloud platform (AWS, Azure, or GCP).
- Hands-on experience with AI and LLM tooling.
Why Join Us?
- The entire team operates remotely, with no office requirement.
- The opportunity to join a geographically distributed team of around thirty people that is growing quickly.
- Direct collaboration with senior leadership and the chance to take on ownership and leadership initiatives.
- Competitive compensation package.
InStore.ai never charges candidates any fee, and all communication will come from an @instore.ai address.
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