Quality Assurance Engineer

Accountabilities: Build and maintain automated end-to-end, component, API, and contract test suites using Python and modern browser automation frameworks, with parallel execution, trace-based debugging, and reliable test architecture. Establish quality gates within CI/CD pipelines, including parallelized test execution, risk-based test selection, and automated promotion criteria. Provision ephemeral, containerized test environments using infrastructure-as-code and support synthetic or masked test-data generation. Monitor test reliability, track flaky tests, improve suite stability, and maintain defined performance and runtime expectations. Validate graph databases and analytics, including schema integrity, relationships, queries, data ingestion, deduplication, and graph-based risk or fraud analysis. Develop evaluation frameworks for AI/ML systems, including accuracy thresholds, model drift, fairness, explainability, and regression testing. Design automated validation for agentic workflows, covering tool calling, orchestration, context management, failure recovery, guardrails, and non-deterministic outputs. Implement testing strategies such as golden datasets, semantic similarity, rubric-based evaluation, and LLM-assisted assessment where appropriate. Collaborate with engineers and data science teams on shift-left quality, feature-level test coverage, root-cause analysis, and feature-pipeline validation. Improve development, testing, and deployment processes, support application onboarding to standardized operating models, and document and escalate issues effectively. Requirements Bachelor’s degree in Computer Science or a related field. Proven hands-on experience designing, developing, and executing automated tests for web applications and services using Python and a modern browser automation framework such as Playwright, Selenium WebDriver, or an equivalent. Strong understanding of maintainable test architecture, including Page Object Model or comparable patterns. Extensive Python scripting experience, with additional scripting knowledge in Unix shell, Ruby, or similar technologies. Experience testing web services and REST APIs using tools such as Postman or Swagger. Working knowledge of graph databases such as Neo4j, Amazon Neptune, TigerGraph, or JanusGraph, including writing and validating graph queries. Experience with SQL and continuous integration platforms such as Jenkins, GitHub Actions, or GitLab CI. Understanding of testing AI/ML-backed features and the differences between deterministic and probabilistic validation approaches. Ability to identify process improvements and communicate technical findings clearly across engineering and cross-functional teams. Experience with agentic frameworks such as LangChain, LangGraph, MCP, or comparable technologies is advantageous. Familiarity with LLM evaluation practices, golden datasets, rubric-based scoring, hallucination and jailbreak testing, and red-teaming is beneficial. Exposure to graph analytics libraries, observability-driven testing, Docker, Kubernetes, Terraform, Ansible, microservices, cloud APIs, and issue-tracking platforms such as JIRA is a plus. Benefits Comprehensive health and wellness plans. Paid time off and company holidays. Flexible, remote-friendly working opportunities. Maternity and paternity leave. Opportunity to work on advanced technologies spanning AI/ML, graph analytics, agentic systems, cloud infrastructure, and automation. Collaborative environment focused on continuous improvement and modern engineering practices. Inclusive workplace committed to equal employment opportunities and diverse teams.