Quality Assurance Engineer

Overview

Quality Assurance Engineer responsible for assuring product quality, building automated tests across microservices and front ends, managing builds and continuous integration, and defining pass/fail criteria for probabilistic graph analytics, ML and agentic AI components.

What you'll do

• Build and maintain end-to-end and component test suites using modern browser automation with auto-waiting, trace-based debugging, and parallel execution as the default
• Shape coverage around the test pyramid, with API and contract tests as the primary safety net and UI reserved for genuine user journeys
• Embed quality gates in CI/CD pipelines, including sharded parallel runs, risk-based test selection, and pass/fail criteria that block promotion without manual sign-off
• Provision ephemeral, containerised test environments on demand through infrastructure-as-code, with synthetic and masked test data generated per run
• Treat test reliability as a product by tracking flake rates, quarantining and fixing unstable tests, and holding suites to explicit stability and runtime SLAs
• Use AI-assisted tooling for test authoring, coverage gap analysis, and locator resilience while owning the judgement on what generated tests are worth
• Work shift-left alongside engineers, contributing test coverage with the feature, and partner on triage to drive issues to root cause
• Design and automate validation for graph database layers, including schema integrity, relationship correctness, and query behaviour across Cypher, Gremlin, or SPARQL
• Build regression coverage for graph analytics outputs such as pathfinding, centrality, community detection, and link analysis used in fraud-ring identification and network risk scoring
• Validate graph ingestion and transformation pipelines for data completeness, deduplication, and referential accuracy at scale
• Develop evaluation harnesses for ML model behaviour, including accuracy thresholds, drift detection, bias and fairness checks, and explainability outputs required for regulatory review
• Build automated test suites for agentic workflows, including tool-calling correctness, multi-step orchestration, context handling, failure recovery, and guardrail enforcement
• Design assertion strategies for non-deterministic outputs, including golden-set comparison, semantic similarity scoring, and LLM-as-judge evaluation
• Establish prompt and model regression testing so behavioural changes are caught before release
• Validate feature pipelines and training/serving consistency in partnership with data science teams
• Incorporate best practices into dev/test/deploy processes and recommend improvements
• Support application onboarding to the defined target operating model to achieve build and release automation
• Document, track, and escalate issues as appropriate

What you'll need

• Bachelor's Degree in Computer Science or a related field
• Proven track record of extensive hands-on experience in automated testing of web applications and services
• Experience designing, developing, and executing automated tests in Python using a modern browser automation framework (Playwright, Selenium WebDriver, or equivalent)
• Demonstrated understanding of maintainable test architecture, such as Page Object Model or equivalent patterns, for end-to-end testing
• Extensive hands-on experience in Python scripting
• Experience testing web services and REST APIs (Postman, Swagger)
• Working knowledge of graph databases such as Neo4j, Amazon Neptune, TigerGraph, JanusGraph or similar, including writing and validating graph queries
• Exposure to testing AI/ML-backed features, with a clear grasp of why probabilistic systems need different assertions than deterministic ones
• Scripting experience beyond Python: Unix shell, Ruby, or similar
• Continuous integration and pipeline-based delivery (Jenkins, GitHub Actions, GitLab CI, or similar)
• Experience with SQL
• Experience recommending process improvements

Nice to have

• Hands-on experience with agentic frameworks such as LangChain, LangGraph, Model Context Protocol (MCP), or comparable orchestration tooling
• Familiarity with LLM evaluation tooling and practices: golden datasets, rubric-based scoring, hallucination and jailbreak testing, red-teaming
• Graph analytics libraries such as Neo4j GDS, Apache Spark GraphX, or NetworkX
• Observability-driven testing, including synthetic monitoring, canary analysis, and validation through traces, logs and metrics
• Docker, Kubernetes, Terraform, Ansible; microservices, container deployment and service orchestration
• Cloud APIs (AWS, Azure, GCP)
• JIRA for issue tracking and escalation

Details

• Location: Bengaluru
• Flexible and remote-friendly opportunities are offered