Quality Assurance Engineer

IndiaRemoteFull-timemid

EngineeringQa Engineer

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Quick Summary

Overview

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

Technical Tools

EngineeringQa Engineer

This is an opportunity to shape quality engineering for a sophisticated, cloud-based decision intelligence platform.
You’ll build automation across microservices, APIs, user interfaces, graph data, AI/ML models, and agentic workflows.
The role combines hands-on test engineering with CI/CD, infrastructure automation, and quality strategy.
You’ll help define reliable validation approaches for systems that can produce probabilistic and context-dependent results.
Working closely with software engineers and data science teams, you’ll bring quality practices into development from the outset.
You’ll also contribute to testing approaches that support reliability, explainability, and regulatory expectations.
The environment is remote-friendly, technically ambitious, and focused on continuous improvement and modern engineering practices.

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

~1 min read

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.