AI Testing Lead

Job description

Data for AI Testing Lead Required Skills \& Experience • 5 years of experience in Data Quality Engineering, Analytics Testing, or Data driven transformation programs. • 3 years leading AI Data Assurance, AI/GenAI, Analytics, or AI Quality Engineering initiatives • Strong knowledge of AI/ML, GenAI, LLMs, various RAG Architectures, Prompt Engineering, Vector Databases, DataOps/MLOps, and AI Governance. • Strong expertise in ETL Testing, Analytics \& BI Testing, Reporting Validation, AI Data Readiness Assurance, AI Data Harness Assurance, AI Data Outcome Assurance and Continuous AI Assurance • Hands-on Experience with Cloud Data \& AI Platforms such as Azure, AWS, GCP, Databricks, Snowflake, Microsoft Fabric, or similar. • Strong leadership, stakeholder management, communication, and mentoring skills Technical \& Professional Requirements • Agile Delivery, Quality Governance • AI Data Assurance, AI/ML, GenAI, LLMs \& RAG Architectures • Data Quality, Data Governance \& Responsible AI • ETL, Data Warehouse, Analytics, BI \& Data Integration Testing • SQL, Snowflake, Databricks, Informatica \& Azure Data Factory (ADF) • Prompt Engineering \& Retrieval Assurance • Python, PySpark \& Test Automation • Playwright, API Testing • Vector Databases, AI Data Pipelines, DataOps \& MLOps • Azure, AWS \& GCP Data \& AI Platforms • Jira, Zephyr, Azure DevOps \& CI/CD Project \& Delivery Leadership • Lead end-to-end delivery of AI Data Assurance programs. • Drive delivery governance, quality metrics, executive reporting, and Agile/Hybrid delivery excellence. Quality Engineering, AI Assurance \& Governance • Define quality strategies, testing frameworks, and assurance processes for AI/ML, GenAI, AI data assurance, analytics, and BI platforms. • Govern end-to-end validation, release readiness, and quality gates. • Lead testing and validation of data platforms, pipelines, analytics solutions, BI platforms and AI-ready datasets. • Implement AI Data Harness Assurance across data pipelines, RAG systems, vector stores, and AI workflows. • Drive AI Data Outcome Assurance by evaluating AI output quality, reliability, explainability, and business alignment. • Support Responsible AI, AI Governance, and Model Assurance initiatives. Automation, Client Orientation \& Team Leadership • Build automation frameworks for AI Data Assurance, BI assurance and continuous quality monitoring. • Embed quality controls and assurance gates within DataOps, MLOps, and CI/CD pipelines. • Lead and mentor AI Data Assurance teams and drive capability development, quality reviews, and continuous improvement. • Collaborate with business, product, data engineering, architecture, AI/ML, and platform teams to deliver AI transformation initiatives. • Drive automation, AI assisted testing, capability development, and continuous improvement initiatives. • Build AI data assurance accelerators and participate in client demos • Contribute to client pursuits, solutioning, proposals, estimations, and AI assurance offerings. • Build partnerships, thought leadership assets, innovation frameworks, webinars, workshops, and knowledge-sharing initiatives.