# Quality Engineer at LSEG

LSEG is hiring a Quality Engineer. Level rates it Works on AI ●●●○; you can [apply on LSEG](https://jobsbylevel.com/go/afcbc20c-4392-4cf8-b8a6-9724f682de06?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. Remote (IND-BLR-Divyasree Technopolis).

## Details

- Company: [LSEG](https://jobsbylevel.com/companies/lseg)
- AI level: AI Level 3 (score 65 out of 100)
- Location: Remote (IND-BLR-Divyasree Technopolis)
- Posted: November 11, 2026
- Apply: https://jobsbylevel.com/go/afcbc20c-4392-4cf8-b8a6-9724f682de06?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=quality-engineer-at-lseg-b6b09e

## Prepare for this job

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## Description

QA Engineer – Data, DevOps & AI Location: Bengaluru, India Work Arrangement: Hybrid Experience: 3–7 years Business Area: AI & Data Engineering Team: Corporate Data Cloud Data Products Employment Type: Full-time About the Role We are seeking a QA Engineer with strong data-testing and automation capabilities to join the Corporate Data Cloud team. You will assure the quality and reliability of cloud-based data and AI solutions through data validation, test automation, CI/CD integration, and foundational testing of Generative AI and Agentic AI applications. The role requires close collaboration with Data Engineering, Platform, DevOps, Product, and Business teams to embed quality throughout the delivery lifecycle. Key Responsibilities Design and execute test scenarios and test cases based on business and functional requirements. Validate end-to-end ETL/ELT pipelines, data transformations, integrations, and business rules. Perform data quality, reconciliation, regression, integration, and large-volume data testing. Develop and maintain automated test frameworks using Python, PyTest, Cucumber, or equivalent tools. Use advanced SQL and Snowflake to validate data and perform root-cause analysis. Integrate automated tests and quality gates into CI/CD pipelines. Support Git-based development practices, deployment validation, configuration checks, log analysis, and release verification. Test Generative AI solutions for accuracy, relevance, groundedness, consistency, hallucination risk, and guardrail effectiveness. Validate Agentic AI workflows, including tool and API interactions, multi-step execution, error handling, retry behaviour, and task completion. Create reusable test datasets and automated evaluation checks for data and AI solutions. Use AI-assisted approaches to improve test-case generation, automation, defect analysis, and QA efficiency. Identify, document, track, and communicate defects, risks, and quality metrics. Collaborate with cross-functional teams to improve engineering quality and release outcomes. Required Skills and Experience 3–7 years of experience in Data QA, ETL testing, data quality engineering, or test automation. Strong SQL skills, including complex queries, joins, aggregations, window functions, and data reconciliation. Hands-on experience with Snowflake and data warehouse or data-platform testing. Experience in Python-based test automation using PyTest or a similar framework. Working knowledge of CI/CD tools such as Azure DevOps, GitHub Actions, Jenkins, or equivalent. Understanding of Git, branching, pull requests, deployment workflows, logging, and monitoring. Familiarity with AWS and/or Azure and modern cloud-based data architectures. Basic understanding of Generative AI concepts, including prompts, grounding, hallucinations, non-deterministic outputs, and guardrails. Basic understanding of Agentic AI concepts, including agents, tools, APIs, orchestration, and multi-step workflows. Experience working in Agile delivery environments using Jira or an equivalent work-management tool. Strong analytical, troubleshooting, communication, and collaboration skills. Preferred Skills Experience working in Financial Services or another regulated industry. Exposure to Infrastructure as Code tools such as Terraform, Bicep, ARM, or CloudFormation. Basic knowledge of Docker or other container technologies. Exposure to cloud monitoring and observability tools. Awareness of AI evaluation frameworks or platforms such as Azure AI Foundry evaluations, RAGAS, DeepEval, or LangSmith. Experience with AI-assisted test generation, automation, and defect analysis. What Success Looks Like Reliable delivery of high-quality data and AI solutions. Increased automated test coverage across data pipelines and CI/CD workflows. Early identification of data, deployment, and AI-quality issues. Effective quality controls covering data accuracy, AI response quality, agent behaviour, and production readiness. Strong collaboration across

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