Data For AI Testing

Job description

***Please apply only if you meet the requirements mentioned in the JD below.*** **Required Skills & Experience** - 8+ years of experience in Data Quality Engineering, Analytics Testing, or Data driven transformation programs. - 5+ 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