Senior Quality Assurance Engineer

Accountabilities

• • Build and maintain end-to-end, component, API, and contract test suites using modern browser automation frameworks, with parallel execution, trace-based debugging, and reliable automation practices.
  • Establish test coverage according to the test pyramid, prioritizing API and contract testing while using UI automation for critical end-user journeys.
  • Integrate automated quality gates into CI/CD pipelines, including parallelized execution, risk-based test selection, and automated release criteria.
  • Provision ephemeral, containerized test environments using infrastructure-as-code and generate appropriate synthetic or masked test data for automated runs.
  • Monitor test reliability through metrics such as flake rates, runtime, and stability SLAs, while proactively fixing or quarantining unreliable tests.
  • Use AI-assisted testing tools for test generation, coverage analysis, and locator resilience while applying sound engineering judgment to validate their effectiveness.
  • Work alongside developers during feature development to embed testing early and collaborate on issue triage and root-cause analysis.
  • Design automated validation for graph databases, including schema integrity, relationships, query behavior, ingestion, transformation, deduplication, and referential accuracy.
  • Build regression testing for graph analytics such as pathfinding, centrality, community detection, and link analysis.
  • Develop evaluation frameworks for AI/ML models covering accuracy, drift, bias, fairness, explainability, and other requirements relevant to regulated environments.
  • Create automated test suites for agentic workflows, validating tool calls, multi-step orchestration, context management, failure recovery, and guardrail enforcement.
  • Develop assertion strategies for non-deterministic outputs using approaches such as golden datasets, semantic similarity scoring, and LLM-based evaluation.
  • Establish prompt and model regression testing to identify unexpected behavioral changes before production releases.
  • Partner with data science teams to validate feature pipelines and consistency between model training and serving environments.
  • Recommend improvements to development, testing, and deployment processes and support application onboarding into standardized build and release practices.
  • Document, track, communicate, and escalate quality issues while maintaining strong standards for product reliability and release readiness.

  Requirements

  • Bachelor’s degree in Computer Science or a related technical discipline.
  • Extensive hands-on experience designing, developing, and executing automated tests for web applications and services using Python and modern browser automation frameworks such as Playwright, Selenium WebDriver, or equivalent.
  • Strong Python scripting skills and experience building maintainable, scalable test automation architectures using Page Object Model or comparable design patterns.
  • Solid experience testing REST APIs and web services using tools such as Postman, Swagger, or similar technologies.
  • Working knowledge of graph databases such as Neo4j, Amazon Neptune, TigerGraph, JanusGraph, or equivalent, including the ability to write and validate graph queries.
  • Understanding of testing AI/ML-powered functionality and the ability to develop appropriate assertions for probabilistic and non-deterministic systems.
  • Experience with SQL and additional scripting technologies such as Unix shell, Ruby, or similar.
  • Practical experience with continuous integration and pipeline-based delivery using Jenkins, GitHub Actions, GitLab CI, or comparable tools.
  • Experience identifying opportunities for process improvement and translating them into practical engineering solutions.
  • Strong analytical and problem-solving skills, with a disciplined approach to debugging and root-cause analysis.
  • Strong communication and collaboration skills, with the ability to work effectively with engineering, data science, and platform teams.
  • Familiarity with agentic frameworks such as LangChain, LangGraph, Model Context Protocol (MCP), or comparable orchestration technologies is advantageous.
  • Knowledge of LLM evaluation practices, including golden datasets, rubric-based scoring, hallucination and jailbreak testing, or red-team approaches, is a plus.
  • Experience with graph analytics technologies such as Neo4j GDS, Apache Spark GraphX, or NetworkX is desirable.
  • Familiarity with observability-driven testing, synthetic monitoring, canary analysis, traces, logs, and metrics is beneficial.
  • Exposure to Docker, Kubernetes, Terraform, Ansible, microservices, cloud deployment, and service orchestration is advantageous.
  • Familiarity with AWS, Azure, or GCP APIs and JIRA for issue tracking is a plus.

  Benefits

  • Comprehensive health and wellness plans.
  • Paid time off and company holidays.
  • Flexible and remote-friendly working opportunities.
  • Maternity and paternity leave.
  • Opportunity to work with advanced AI/ML, graph analytics, and agentic technologies.
  • Meaningful ownership in shaping modern QA and automated testing practices.
  • Collaborative environment with opportunities to work across engineering, data science, and platform teams.
  • Exposure to enterprise-grade cloud, microservices, CI/CD, and infrastructure technologies.
  • Inclusive workplace committed to diversity, equal opportunity, and professional development.