01Overview
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. .
