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 • Robust 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.

📌 AI Testing Lead (Bengaluru)
🏢 Infosys
📍 Bengaluru