Job Description:

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

Key Responsibilities:

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

Technical Requirements:

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

Additional Responsibilities:

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

Preferred Skills:

Technology->AI-Responsible AI->Responsible AI->explainable ai,Technology->Data Services Testing->Big Data Testing,Technology->Experience Engineering->GenerativeUI/DynamicUI

Data Testing Cloud Consultant