Working Timing (6:30 pm - 2:30 am)
Work mode: Remote
Experience Required: 3-6 Years
Salary Package: 12- 24 LPA
Job Summary
We are looking for a Senior Automation Test Engineer to lead the adoption of AI-powered software testing methodologies, where intelligent agents, AI-assisted test generation, self-healing automation, risk-based test optimization, and natural-language-driven testing become the primary approach to quality engineering. . The role combines hands-on automation engineering with the design and implementation of modern AI-assisted testing frameworks, integrating conventional automation where appropriate while driving the transition toward AI-first quality engineering.
Beyond building automation, the position involves understanding why a testing practice may not be landing with a particular team and adjusting the approach - which requires reading organizational context, not just technical systems.
Key Responsibilities
Define the AI Testing Practice
Design AI-first testing strategies, implementation frameworks, governance standards, and best practices that enable consistent adoption across engineering teams.
Evaluate emerging AI-powered testing platforms, autonomous testing agents, and automation frameworks, recommending the most suitable approach for each project.
Define how AI is applied throughout the testing lifecycle—including requirements analysis, test generation, test maintenance, execution, defect triage, root-cause analysis, and reporting—while establishing appropriate human review and governance checkpoints.
Establish metrics and success criteria to measure the effectiveness, quality, and business value of AI-assisted testing.
Build & Implement Automation
Design, build, and maintain automation frameworks and scripts for web, mobile, API, and integration testing.
Integrate AI-powered testing capabilities into CI/CD pipelines, enabling automated quality gates, intelligent release validation, and continuous feedback.
Roll out testing practices directly with engineering teams and, where required, at client locations
Drive AI Adoption & Engineering Enablement
Assess the testing maturity of engineering teams and create practical adoption roadmaps for transitioning from conventional automation to AI-assisted testing.
Mentor engineers and testers on AI-enabled testing techniques, responsible AI usage, prompt engineering, and modern quality engineering practices.
Establish reusable templates, playbooks, and implementation patterns that allow AI-powered testing methodologies to scale across multiple projects and teams.
Improve Through Feedback
Identify why a testing approach or automation practice isn't being adopted by a team, and adjust it based on the root cause rather than assumption.
Capture what works and what doesn't from each rollout and feed it back into shared standards and frameworks.
Coordinate with development and deployment teams to ensure testing connects smoothly with what comes before and after it in the delivery pipeline.
Required Skills
Automation & Quality Engineering
3–6 years of hands-on experience in automation testing and quality engineering, across web, mobile, API, and integration testing.
Strong hands-on experience with automation frameworks such as Playwright, Selenium, Cypress, Appium, or similar.
Experience designing, building, and maintaining scalable automation frameworks, not just individual test scripts.
Strong understanding of testing methodologies, test design techniques, and quality assurance best practices.
Experience integrating automated testing into CI/CD pipelines using tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
Strong understanding of release validation, deployment testing, smoke testing, and quality gates.
Strong understanding of Git and collaborative development workflows.
AI Tools & Usage
Hands-on, daily experience using AI-powered testing tools and assistants for test generation, automation development, defect analysis, and reporting.
Working knowledge of more than one AI tool or model, and the judgment to choose the right one for a given task.
Experience evaluating new or emerging AI testing tools and recommending which to adopt.
Ability to write clear, effective prompts and instructions to get accurate, usable output from AI tools, and to catch and correct AI mistakes.
A genuine, active interest in keeping up with new AI tools, models, and techniques as they evolve.
Communication
Strong written and spoken communication skills in English
Strong interpretation skills: the ability to read organizational and stakeholder context, understand why a process or practice isn't working, and not treat every implementation issue as a technical one.
Ability to clearly explain testing strategy, findings, and AI-adoption recommendations to both technical and non-technical stakeholders, including in client-facing settings.