AI Automation with AWS Bedrock
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Role details
Tech stack
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Job description
· Design, develop, and maintain test automation frameworks using Python and/or Java. · Build automated test suites for functional, regression, performance, and integration testing. · Participate in testing of AI/ML models, including validating outputs, datasets, model accuracy, and edge-case behaviors. · Implement AI-assisted testing tools and contribute to developing intelligent test solutions. · Collaborate with cross-functional teams to understand product requirements and convert them into test strategies. · Identify root causes of defects and work with engineering teams to resolve them. · Perform code reviews, maintain test documentation, and ensure best testing practices. · Contribute to CI/CD pipelines with automated quality gates. · Continuously evaluate and integrate new tools, frameworks, and AI capabilities to improve test efficiency.
Requirements
Must have; Need a strong Sr Automation AWS BEDROCK consultant with 10+ Years experience with Strong AI AWS Bedrock, · Bachelor’s degree in computer science, Engineering, or related field (or equivalent experience). · Strong experience in test automation using Python and/or Java. · Solid understanding of testing methodologies, QA processes, and automation principles. · Exposure to AI/ML concepts such as model training, model inference, datasets, bias, and evaluation metrics. · Experience working with API testing tools (e.g., Postman, REST Assured, PyTest). · Hands-on experience with automation frameworks like Selenium, Playwright, Cypress, or Robot Framework. · Familiarity with CI/CD platforms (Jenkins, GitHub Actions, GitLab CI, etc.). · Strong analytical and debugging skills. Preferred Qualifications · Experience testing AI/ML pipelines or platforms. · Understanding of LLMs, NLP, generative AI, or ML workflows. · Knowledge of cloud platforms (AWS, Azure, Google Cloud Platform). · Experience with containerization tools (Docker, Kubernetes). · Familiarity with data validation, data quality tools, or synthetic data generation. · Experience with test-case generation using AI tools.
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