Job Description

  • Design and execute manual and automated test cases  for backend APIs, frontend interfaces, and end-to-end AI workflows.
  • Conduct exploratory testing  of new generative AI features and user interactions.
  • Develop and maintain automated test frameworks  for UI and API.
  • Build automation scripts for AI/ML pipelines  using pytest, unittest, or custom frameworks.
  • Create comprehensive test plans , manage defects, and report results using Jira, or similar tools.
  • Validate RAG (Retrieval-Augmented Generation)  pipelines, including embedding accuracy, retrieval quality, and model response evaluation.
  • Test AI agents and orchestration flows , ensuring correctness in multi-step reasoning and tool invocations.
  • Perform performance and scalability testing  for ML inference endpoints (SageMaker, Bedrock, custom APIs), monitoring latency and throughput.
  • Conduct integration testing  across data ingestion, preprocessing, inference, and output pipelines.
  • Validate UI/UX and Gen AI interaction flows , ensuring a consistent and intuitive user experience across devices and browsers.
  • Automate end-to-end test scenarios , including document upload, retrieval, summarization, and inference workflows.
  • Integrate and maintain automated test suites  within CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins).
  • Collaborate closely with ML engineers, data scientists, and developers to reproduce issues, validate fixes, and ensure model reliability.
  • Ensure security, compliance, and data privacy  standards are upheld for sensitive datasets during testing.

    • Skills & Experience

  • Strong experience in manual testing  of backend APIs, frontend apps, and full-stack systems.
  • Hands-on with test automation tools : Selenium, Playwright, Cypress, Postman, REST-assured, or Python-based frameworks.
  • Proficient in test case design , bug reporting, and defect tracking (Jira, Trello, or equivalents).
  • Familiarity with AI/ML workflows  — especially LLM-based applications, RAG pipelines, embeddings, and model inference.
  • Experience with performance testing  tools or frameworks; understanding of latency, throughput, and scaling metrics.
  • Knowledge of CI/CD automation  and integrating tests into build pipelines.
  • Understanding of API testing principles : CRUD operations, authentication, edge cases, and error handling.
  • Familiar with UI/UX validation  and cross-browser compatibility testing.
  • Strong analytical and communication skills — able to describe AI-related issues clearly and reproducibly.
  • Awareness of security and compliance  practices for testing sensitive or proprietary data.

    • Education:

  • A Master’s degree in Machine Learning, Computer Science with a preference for specialization in the NLP domain.

    • We are an equal-opportunity employer that values diversity at all levels. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, national origin, age, sexual orientation, gender identity, disability, or veteran status.

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