Educational Requirements

Bachelor of Engineering

Service Line

Infosys Quality Engineering

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.

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

Technical and Professional Requirements

- 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.

- Solid 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

Preferred Skills

- Technology->AI-Data science->PYTHON

- Technology->AI-Generative AI->Generative AI - Basic

📌 Data For AI Testing Lead (Bengaluru)
🏢 Infosys
📍 Bengaluru