Data For AI Testing Lead

Job DescriptionWe are seeking a Quality Engineering Lead to drive the delivery of AI Data Assurance initiatives by ensuring trusted, high-quality, and AI-ready data foundations. This role is responsible for defining quality strategies, establishing AI Data assurance frameworks, driving automation, and ensuring trusted, high-quality, AI-ready data foundations that enable reliable, responsible, and business-aligned AI outcomes.
The ideal candidate will have strong experience in Data Testing, AI Data Assurance, Analytics Testing, AI/ML Data Validation, and Quality Engineering, along with a solid understanding of AI/GenAI ecosystems, LLMs, RAG architectures, DataOps/MLOps, and Responsible AI practices
Roles ResponsibilitiesProject 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.
Technical RequirementRequired 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
Additional ResponsibilityTechnical 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
Educational RequirementBachelor of Engineering
Preferred SkillsTechnology->AI-Generative AI->Generative AI - Basic,Technology->AI-Data science->PYTHON
Service LineInfosys Quality Engineering