Snr Test Automation Lead

Job Title: Senior Test Automation Lead – Playwright (AI/ML Focus)

Location: Hyderabad

Job Type: Full-Time

Experience Required: 8+ years in Software QA/Testing, 3+ years in Test Automation using Playwright, 2+ years in AI/ML project environments

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About the Role:

We are seeking a passionate and technically skilled Senior Test Automation Lead with deep experience in Playwright-based frameworks and a solid understanding of AI/ML-driven applications. In this role, you will lead the automation strategy and quality engineering practices for next-generation AI products that integrate large-scale machine learning models, data pipelines, and dynamic, intelligent UIs.

You will define, architect, and implement scalable automation solutions across AI-enhanced features such as recommendation engines, conversational UIs, real-time analytics, and predictive workflows, ensuring both functional correctness and intelligent behavior consistency.

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Key Responsibilities:

Test Automation Framework Design & Implementation

· Design and implement robust, modular, and extensible Playwright automation frameworks using TypeScript/JavaScript.

· Define automation design patterns and utilities that can handle complex AI-driven UI behaviors (e.g., dynamic content, personalization, chat interfaces).

· Implement abstraction layers for easy test data handling, reusable components, and multi-browser/platform execution.

AI/ML-Specific Testing Strategy

· Partner with Data Scientists and ML Engineers to understand model behaviors, inference workflows, and output formats.

· Develop strategies for testing non-deterministic model outputs (e.g., chat responses, classification labels) using tolerance ranges, confidence intervals, or golden datasets.

· Design tests to validate ML integration points: REST/gRPC API calls, feature flags, model versioning, and output accuracy.

· Include bias, fairness, and edge-case validations in test suites where applicable (e.g., fairness in recommendation engines or NLP sentiment analysis).

End-to-End Test Coverage

· Lead the implementation of end-to-end automation for:

o Web interfaces (React, Angular, or other SPA frameworks)

o Backend services (REST, GraphQL, WebSockets)

o ML model integration endpoints (real-time inference APIs, batch pipelines)

· Build test utilities for mocking, stubbing, and simulating AI inputs and datasets.

CI/CD & Tooling Integration

· Integrate automation suites into CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI, or similar.

· Configure parallel execution, containerized test environments (e.g., Docker), and test artifact management.

· Establish real-time dashboards and historical reporting using tools like Allure, ReportPortal, TestRail, or custom Grafana integrations.

Quality Engineering & Leadership

· Define KPIs and QA metrics for AI/ML product quality: functional accuracy, model regression rates, test coverage %, time-to-feedback, etc.

· Lead and mentor a team of automation and QA engineers across multiple projects.

· Act as the Quality Champion across the AI platform by influencing engineering, product, and data science teams on quality ownership and testing best practices.

Agile & Cross-Functional Collaboration

· Work in Agile/Scrum teams; participate in backlog grooming, sprint planning, and retrospectives.

· Collaborate across disciplines: Frontend, Backend, DevOps, MLOps, and Product Management to ensure complete testability.

· Review feature specs, AI/ML model update notes, and data schemas for impact analysis.

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Required Skills and Qualifications:

Technical Skills:

· Strong hands-on expertise with Playwright (TypeScript/JavaScript).

· Experience building custom automation frameworks and utilities from scratch.

· Proficiency in testing AI/ML-integrated applications: inference endpoints, personalization engines, chatbots, or predictive dashboards.

· Solid knowledge of HTTP protocols, API testing (Postman, Supertest, RestAssured).

· Familiarity with MLOps and model lifecycle management (e.g., via MLflow, SageMaker, Vertex AI).

· Experience in testing data pipelines (ETL, streaming, batch), synthetic data generation, and test data versioning.

Domain Knowledge:

· Exposure to NLP, CV, recommendation engines, time-series forecasting, or tabular ML models.

· Understanding of key ML metrics (precision, recall, F1-score, AUC), model drift, and concept drift.

· Knowledge of bias/fairness auditing, especially in UI/UX contexts where AI decisions are shown to users.

Leadership & Communication:

· Proven experience leading QA/Automation teams (4+ engineers).

· Strong documentation, code review, and stakeholder communication skills.

· Experience collaborating in Agile/SAFe environments with cross-functional teams.

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Preferred Qualifications:

· Experience with AI Explainability frameworks like LIME, SHAP, or What-If Tool.

· Familiarity with Test Data Management platforms (e.g., Tonic.ai, Delphix) for ML training/inference data.

· Background in performance and load testing for AI systems using tools like Locust, JMeter, or k6.

· Experience with GraphQL, Kafka, or event-driven architecture testing.

· QA Certifications (ISTQB, Certified Selenium Engineer) or cloud certifications (AWS, GCP, Azure).

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Education:

· Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related technical discipline.

· Bonus for certifications or formal training in Machine Learning, Data Science, or MLOps.

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Why Join Us?

· Work on cutting-edge AI platforms shaping the future of [industry/domain].

· Collaborate with world-class AI researchers and engineers.

· Drive the quality of products used by [millions of users / high-impact clients].

· Opportunity to define test automation practices for AI—one of the most exciting frontiers in tech.