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Job Description Quality Engineering Lead Role Overview 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. 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. 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 Solid 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 Compensation: 1,800,000.00 - 2,000,000.00 per year Work Location: Remote .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.