Role Overview
This is a hands-on Quality Engineering transformation leadership role. The successful candidate will define enterprise standards, influence Product, Engineering, DevOps, Platform Engineering and SRE teams, and turn emerging AI and automation capabilities into scalable engineering practices. The objective is to increase delivery speed without compromising resilience, regulatory requirements or customer experience.
Key Responsibilities
Expand each area to explore the role's core expectations.
01. Quality Engineering Leadership
- Define and execute enterprise Quality Engineering strategy, standards, governance and best practices.
- Champion Shift-Left, Shift-Right, Continuous Testing and Quality-by-Design.
- Lead, mentor and develop Quality Engineering teams while improving quality and productivity.
- Remain hands-on when evaluating emerging technologies, building proofs of concept and setting technical direction.
- Scale successful engineering solutions into enterprise-wide adoption.
02. AI-Driven Quality Engineering
- Apply AI and GenAI to test case generation, automation development, synthetic test data, defect triage and root-cause analysis.
- Use risk-based testing and AI-assisted regression optimization to improve testing efficiency.
- Build AI-powered QA copilots, self-healing frameworks, autonomous testing solutions and predictive quality analytics.
- Integrate AI agents with Jira, Confluence, GitHub, ServiceNow, BrowserStack and observability platforms.
03. Automation & Quality Platform Engineering
- Develop enterprise automation frameworks for UI, API, integration, contract, performance, security, accessibility and end-to-end testing.
- Establish scalable testing platforms, reusable accelerators and quality gates.
- Integrate continuous testing into CI/CD pipelines and improve automation maturity across teams.
- Promote automation-first engineering practices and reusable testing capabilities.
04. Cloud, Data & Reliability Engineering
- Define testing strategies for cloud-native, microservices, event-driven, distributed and AWS-based systems.
- Lead testing of Kafka, Elasticsearch, REST APIs, messaging systems and data platforms.
- Partner with SRE teams on observability, resilience, failover, chaos testing and incident analysis.
- Drive reliability improvements across distributed systems and production environments.
05. Performance Engineering
- Establish performance engineering frameworks and automate performance tests within CI/CD pipelines.
- Validate scalability, reliability and capacity planning assumptions.
- Identify performance bottlenecks and support engineering teams in resolving them.
Ideal Candidate
A hands-on Quality Engineering Transformation Leader
An experienced QA/QE leader with 15+ years in software testing, automation or related engineering domains, a track record of managing teams and leading enterprise quality transformations, and strong knowledge of Agile, DevOps, CI/CD and Continuous Delivery. The role calls for someone who can set strategy, validate technology through practical proofs of concept, and help teams adopt AI-enabled testing at enterprise scale.
Technical Skills
Programming Languages
Java, Python, JavaScript and TypeScript.
Test Automation
Playwright (preferred), Selenium, BrowserStack, Cucumber and Gherkin.
API Testing
Postman, REST Assured, SoapUI and Swagger/OpenAPI.
Performance Testing
JMeter and k6 for performance validation and automated testing.
AWS & Cloud Services
EC2, Lambda, API Gateway, CloudWatch, S3 and Glue.
DevOps & Containers
Jenkins, Git pipelines, Docker and Kubernetes.
Data & Observability
Kafka, Elasticsearch, SQL, NoSQL, Datadog, Grafana, Prometheus and ELK.
AI Quality Engineering
AI-generated testing, autonomous and self-healing automation, defect prediction, RCA, visual AI testing, predictive analytics and synthetic test data.
Preferred Domain Experience
Adobe Experience Manager (AEM), AI engineering platforms, quality observability, cloud migration and digital transformation programs.
Engineering Practices
Agile, DevOps, CI/CD, Continuous Delivery and Quality Engineering governance.
Success Metrics
Targets stated in the supplied job description.
Additional measures of engineering success
- Improve release confidence and deployment success rate.
- Strengthen system reliability and operational resilience.
- Increase engineering productivity and continuous testing adoption.
- Improve customer experience while maintaining regulatory requirements.
About London Stock Exchange Group (LSEG)
LSEG is a global financial markets infrastructure and data provider. Its stated purpose is to drive financial stability, empower economies and enable customers to create sustainable growth.
The job description describes an organisation of approximately 25,000 people across 65 countries, guided by the values of Integrity, Partnership, Excellence and Change.
LSEG highlights inclusion, innovation, sustainability and employee support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
The listing also notes recognition as a Great Place to Work in India in 2025.
Ready to lead Quality Engineering transformation?
Review the original job listing and application requirements on LinkedIn.