Senior Quality Assurance Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Quality Assurance Engineer based in India.

This is a senior QA engineering role focused on building reliable, scalable quality systems for modern AI-powered enterprise platforms.

You will design automated testing strategies across web applications, APIs, microservices, graph databases, and cloud environments.

The role goes beyond traditional testing, requiring thoughtful approaches to probabilistic AI/ML and agentic workflows where quality must be evaluated contextually.

You will help establish automated quality gates, ephemeral test environments, regression frameworks, and measurable test reliability standards.

Working closely with engineering, data science, and platform teams, you will drive quality earlier in the development lifecycle and help identify root causes quickly.

You will also contribute to testing graph analytics, model behavior, data pipelines, and AI-generated outputs with a strong focus on reliability, fairness, and explainability.

This is an opportunity to shape QA practices for sophisticated technologies while having meaningful ownership in a collaborative, remote-friendly engineering environment.

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.