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About the role
We are looking for a Principal Quality Engineer (AI Automation Architect) to join us in Bengaluru. 
 
As part of our Platform Quality Engineering group, you will own and drive the AI strategy, architecture and adoption for test automation across the organisation. This is a senior, hands-on quality engineering role focused on defining standards and guardrails, building AI-assisted quality engineering patterns and helping teams use AI safely and effectively across the software testing lifecycle. 
 
You will work closely with quality engineers, Engineering, Product and DevOps teams to evolve how automated tests are created, maintained, executed, triaged and reported. You will keep our approach aligned with changes in AI tooling and models, run enablement programmes and ensure AI-assisted automation is practical, controlled and embedded into day-to-day quality engineering work. 

What you’ll do 

• Own the end-to-end AI test automation architecture, including approved tools, models and how they integrate with the QE technology stack.  
• Define how AI-assisted workflows connect with Selenium, Playwright, Cucumber, BDD, API clients, CI/CD pipelines and existing quality engineering tools.  
• Establish where human review is mandatory for AI-assisted test design, automation code, test data, documentation and reporting outputs.  
• Maintain a living roadmap that tracks industry and vendor changes and translates them into practical, approved quality engineering patterns.  
• Define and maintain QE standards, including prompt libraries, repository templates, agent skills, naming conventions, model selection, cost controls and quality gates.  
• Set clear data-handling guardrails, including restrictions on secrets, personal data and sensitive information in prompts, fixtures and AI-assisted workflows.  
• Build and enhance AI-enabled QE capabilities, including intelligent test generation, automation code generation, flaky-test analysis, failure analysis, framework maintenance and reporting.  
• Integrate AI workflows into CI/CD and QE tooling so AI-assisted work follows the same review, versioning, execution and governance paths as traditional automation.  
• Pilot new capabilities, scale what works and retire what does not, using clear success criteria and evidence from real delivery teams.  
• Train and coach quality engineers on effective AI use, including prompting, reviewing AI output, test design with agents, debugging and knowing when not to rely on AI.  
• Enforce human-in-the-loop review for AI-generated tests, supported by review rubrics and automated checks such as linting, secret scanning and stability runs.  
• Guard against quality engineering debt from unchecked AI output, including flaky tests, brittle locators, duplicate coverage and leaked sensitive data.  
• Partner with product and engineering teams to ensure AI-assisted quality engineering supports reliable delivery rather than adding unreviewed automation volume.  
• Continue to ship reference solutions, code, pipelines and reusable patterns that teams can adopt across products.  

What we’re looking for 

• 12+ years’ experience in software quality, test automation, QE platform engineering or a comparable role, including 3+ years influencing automation strategy at scale.  
• Practical experience using LLMs and AI coding agents in real quality engineering workflows, including test generation, refactoring, debugging and review.  
• Proven ability to design quality engineering architecture, define guardrails and ship reference solutions hands-on.  
• Deep hands-on experience with modern test automation tools such as Playwright, Selenium, Cypress, REST or API frameworks, BDD, Cucumber and CI/CD.  
• Experience using agentic IDEs, Copilot-class tools, Claude, MCP or custom agent skills for domain-specific automation.  
• Track record of architecting multi-product quality engineering programmes while still contributing to code, pipelines and framework implementation.  
• Experience supporting multiple product teams through shared frameworks, standards and platform quality engineering practices.  
• Strong understanding of how to review, govern and validate AI-generated tests, code and documentation.  
• Working knowledge of Agile or Scrum delivery practices.  
• Good analytical and problem-solving skills, with attention to detail and the ability to work through complex technical issues.  
• Clear communication skills, with the ability to coach engineers, explain standards and influence adoption across Agile teams.  
• Financial services industry experience is preferred. 

Why EquiLend? 

• Be recognised for your contribution through a discretionary annual cash bonus, linked to individual and company performance.  
• Work flexibly with up to 100 remote working days per calendar year.  
• Take time to recharge with generous annual leave, plus public holidays.  
• Access comprehensive health insurance for you and your dependents from your first day.  
• Receive personal accident insurance from the start of your employment.  
• Connect with colleagues at an annual company-sponsored team retreat, hosted at a new location each year.  
• If relocating from more than 40 miles away, receive hotel accommodation and travel ticket reimbursement.