Senior Python Automation Test Engineer with AI
EPAM Systems
Responsibilities
- Design, develop, enhance and maintain scalable test automation frameworks using Python
- Build robust automation solutions using PyTest, Behave, Robot Framework, Selenium and Playwright
- Lead framework architecture decisions focused on scalability, maintainability and reusability
- Implement parallel execution strategies and optimize execution times across browsers and platforms
- 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
- Integrate automation suites with CI/CD pipelines using Jenkins and GitHub Actions
- Define test strategies, automation roadmaps and quality metrics
- Collaborate with QA, Development, Product and Business teams
- Mentor team members on Python, automation frameworks, design patterns and quality engineering practices
- Apply AI-driven solutions for automated test case generation, test script generation and code reviews
- 5-9 years of experience in test automation engineering
- Proficiency in Python including Object-Oriented Programming principles, decorators and exception handling
- Expertise in PyTest, Behave and Robot Framework combined with Selenium and Playwright
- Skills in API testing including authentication mechanisms such as Basic Authentication, Bearer Tokens and OAuth
- Knowledge of software design patterns including Singleton, Factory and Page Object Model
- Familiarity with CI/CD tools such as Jenkins and GitHub Actions including static code analysis and quality gates
- Understanding of Test Pyramid principles, Definition of Ready and Definition of Done
- Hands-on experience with LLMs such as GitHub Copilot, Claude and Cursor
- Understanding of Prompt Engineering, Context Window Management and Tokenization
- Skills in generating and utilizing embeddings for semantic search applications including similarity search and retrieval mechanisms
- Background in designing AI-driven solutions using tools such as Claude, GitHub Copilot and Cursor
- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering or related field
- Knowledge of RAG architectures and experience integrating LLMs with enterprise knowledge sources
- Understanding of Model Context Protocol concepts and ability to orchestrate agent interactions and task delegation
- Understanding of Attention Mechanisms and Transformer-based model architectures
- Capability to build multi-agent workflows for end-to-end testing, automated reporting and failure recovery