Senior Python Automation Test Engineer with AI
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Key skills for this role
Role Overview
Design scalable automation frameworks, drive quality engineering practices, and use Generative AI and Agentic AI to accelerate software testing and delivery.
Provide technical leadership in framework architecture and enterprise-wide automation initiatives.
Key Skills for This Role
Full Job Posting
Role overview
Design scalable automation frameworks, drive quality engineering practices, and use Generative AI and Agentic AI to accelerate software testing and delivery.
Provide technical leadership in framework architecture and enterprise-wide automation initiatives.
Automation engineering
- Design, develop, enhance, and maintain scalable test automation frameworks using Python.
- Build automation solutions with PyTest, Behave, Robot Framework, Selenium, and Playwright.
- Implement parallel execution and optimize execution times across browsers and platforms.
API and quality engineering
- Establish API automation solutions for REST and SOAP services.
- Validate XML and JSON payloads through serialization and deserialization techniques.
- Conduct code reviews and enforce clean coding standards.
- Define test strategies, automation roadmaps, and quality metrics.
Delivery and collaboration
- Integrate automation suites with Jenkins and GitHub Actions CI/CD pipelines.
- Collaborate with QA, Development, Product, and Business teams.
- Mentor team members on Python, automation frameworks, design patterns, and quality engineering.
AI-enabled testing
- Apply AI-driven solutions for automated test case generation, test script generation, and code reviews.
- Use LLMs, embeddings, semantic search, and retrieval mechanisms in testing solutions.
Core qualifications
- 5 to 9 years of experience in test automation engineering.
- Proficiency in Python, including object-oriented programming, decorators, and exception handling.
- Expertise in PyTest, Behave, Robot Framework, Selenium, and Playwright.
- Experience with API testing, authentication mechanisms, and software design patterns.
- Familiarity with Jenkins and GitHub Actions, static code analysis, and quality gates.
AI qualifications
- Hands-on experience with LLMs such as GitHub Copilot, Claude, and Cursor.
- Understanding of prompt engineering, context window management, and tokenization.
- Experience generating and using embeddings for semantic search, similarity search, and retrieval.
- Knowledge of RAG architectures, Model Context Protocol, transformer architectures, or multi-agent workflows is preferred.
Education
- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
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