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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # QA Validation Engineer for AI Initiatives (Teradyne, North Reading, MA) - **Company:** Teradyne Inc. - **Location:** North Reading, MA, United States - **Experience:** Experienced - **Salary:** $126,100.0 - $201,800.0 - **Contract:** Permanent contract - **Skills:** Testing (Software), Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Databases, Continuous Integration, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Profiling, Data Retrieval, Python (Programming Language), Machine Learning, Regression Testing, SQL Databases, Software Testing Automation Framework, Data Streaming, Strategies of Testing, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Usage Tracking, Data Lakes, Integration Tests, Information Technology, Data Lineage, Machine Learning Operations, Software Coding, Automation Anywhere, Programming Languages - **Published:** August 19, 2026 - **Apply:** https://dejobs.org/x/x/E234FC644A5047EA90A471AC01D58E78/job/ ## About the Role * Bachelor's degree in business, information technology or related field (Master's in Computer Science, Data Science, Engineering, or related field preferred). Experience * 7+ years in QA/validation engineering; 2+ years focused on AI/ML or data-intensive systems. * Preferably a software test automation engineer with hands-on coding experience. He/she should also have a solid understanding of databases to assess structural correctness, along with familiarity with regulatory and compliance standards to support or lead audit-related activities. Technical Skills * Programming languages such as Python, Java, or similar; SQL; test automation frameworks; ML metrics; and data profiling tools. * Generative AI, agent orchestration, prompt engineering advanced skills. * Data lineage, data governance, schema validation, ETL/ELT testing, data lake/warehouse structures. * Familiar with AI ethics, risk management, audit processes. * Leadership, cross-functional collaboration, change management, technical documentation, stakeholder communication. Key Competencies * Analytical Expertise: Advanced skills in data analysis, process optimization, and problem-solving. * Communication: Exceptional written, verbal, and visual communication skills, with the ability to engage both technical and non-technical audiences. * Collaboration: Proven ability to work effectively with stakeholders across multiple levels and departments, including balancing competing priorities (negotiation skills). * Adaptability: Ability to manage ambiguity and shifting priorities in a fast-paced environment. Preferred Qualifications * Experience with multi-agent testing and API integration validation. * Knowledge of AI-human interaction design and workflow optimization. * Certifications in AI/ML, data governance, or quality engineering. * Experience establishing QA Centers of Excellence for AI programs. ## Description The QA/Validation Lead Engineer for AI Initiatives is responsible for leading the end-to-end validation, testing, and quality assurance of all AI/ML systems from a data-centric perspective. This role ensures that proper data sources, structures, pipelines, and governance frameworks are validated and maintained across all AI initiatives, including generative AI, agentic systems, and human-in-the-loop workflows. The role combines strategic QA leadership with hands-on technical validation to safeguard the accuracy, reliability, compliance, and ethical use of data powering AI solutions. This position reports to the IT Quality Assurance Manager . * Validate that AI/ML models are consuming accurate, authorized, and properly structured data sources. * Design and execute data quality test strategies to assess completeness, consistency, lineage, and timeliness of training and inference data. * Identify and flag data hallucinations, logic errors, and edge cases in AI model outputs traceable to data issues. * Develop and lead comprehensive test strategies for AI/ML systems, including accuracy, bias, robustness, and regression testing. * Oversee scenario-based testing and output validation for generative AI and LLM-driven applications. * Automate validation suites for agentic/multi-agent systems, integration testing, and CI/CD pipelines for ML models. * Assess AI model performance through both manual and automated review, with emphasis on data quality impact on outputs. * Validate prompt engineering outputs from a data accuracy standpoint, ensuring responses are grounded in verified data sources. * Conduct scenario testing to identify cases where data gaps or structural flaws produce unreliable outputs. * Partner with prompt engineers to optimize data retrieval and grounding strategies. * Ensure all AI data sources and structures meet governance, regulatory, and compliance standards. * Lead audit processes, documentation, and risk assessments related to data usage in AI systems * Conduct bias detection and explainability testing to ensure ethical and compliant AI use * Maintain traceability of data lineage for regulatory reporting and audit readiness. * Validate data flows across human-plus-AI workflows and agent orchestration systems. * Ensure seamless data integration across multi-agent architectures and API interfaces. * Collaborate with data engineering, data science, AI/ML engineering, and product teams to establish data quality standards. All About You We seek individuals who share our passion and determination. Our commitment to customer success drives us to go the extra mile. If you're ready to join us in this mission, take a closer look at the minimum criteria for the position. ## Related Videos - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI as a Test Designer: Transforming Experience into Automated Testing](https://www.wearedevelopers.com/videos/1984-ai-as-a-test-designer-transforming-experience-into-automated-testing) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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