We 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

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.

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