We are seeking a talented QA Engineer to join our dynamic team. What is this project about? An AI-powered analytics platform built for the defense domain. It ingests and analyzes data from multiple sources, automatically surfacing key findings and generating actionable insights and recommendations. By automating heavy manual analysis, the platform helps organizations make faster, better-informed decisions, reduce manual effort, and improve overall operational performance. What is the team size and structure? Project Manager, Software Architect, ML Engineers, UI/UX Designer, DevOps, Business Analyst. What is the interview process like? * Recruiter interview — up to 30 minutes; * Technical interview — up to 1.5 hours; * PM interview — up to 1 hour; * Polygraph screening.
Requirements: * 5+ years of experience in software quality assurance; * Strong experience in manual testing of complex web applications; * Good understanding of software development lifecycle and QA methodologies; * Experience testing RESTful HTTP APIs using Postman, OpenAPI/Swagger, or similar tools; * Experience testing Server-Sent Events (SSE), including streaming responses, long-running operations, cancellation, reconnection, and error recovery; * Experience validating data across relational databases, search indexes, object storage, caches, and asynchronous processing pipelines; * Experience with SQL for database validation; * Experience testing frontend applications, backend APIs, and end-to-end business workflows; * Experience with test planning, test design, execution, and defect management; * Experience working in Agile/Scrum teams; * Understanding of authentication and authorization concepts, including OIDC/OAuth 2.0, RBAC, ABAC, and security testing fundamentals; * Experience verifying that restricted or sensitive data is not exposed through APIs, search results, logs, caches, or frontend state; * Automation experience using Playwright, Cypress, Selenium, or similar frameworks; * Experience writing and maintaining automated UI and/or API tests; * Ability to identify repetitive test scenarios suitable for automation; * Strong analytical thinking, attention to detail, and ownership mindset; * Intermediate level of English.
Nice-to-have: * Experience with Playwright; * Experience testing AI-powered, ML, or data analytics platforms; * Experience testing GIS or map-based applications (MapLibre, Mapbox, Leaflet); * Experience working on Defense/MilTech projects; * Experience testing FastAPI-based backend services; * Experience with PostgreSQL, Redis, OpenSearch, and S3-compatible object storage; * Experience with performance and load testing tools such as k6, Locust, or JMeter; * Understanding of LLM-specific risks, including hallucinations, prompt injection, unsupported claims, unsafe tool execution, nondeterministic outputs, and incomplete source grounding; * Understanding of geospatial concepts such as WGS84, MGRS, GeoJSON, polygons, radius search, and area intersection.
Responsibilities: * Perform manual functional, regression, integration, exploratory, and acceptance testing; * Validate frontend applications, backend APIs, and end-to-end business workflows; * Test AI-generated outputs for correctness, consistency, and usability from the end-user perspective; * Verify data integrity across frontend, backend, databases, and external integrations; * Test map-based functionality, geospatial visualizations, filters, and user interactions; * Create and maintain test cases, test plans, checklists, and QA documentation; * Report, prioritize, and track defects throughout their lifecycle; * Collaborate closely with Product Manager, Business Analyst, Developers, ML Engineers, and UI/UX Designer to ensure product quality; * Participate in backlog refinement, sprint planning, estimation, and release activities; * Identify repetitive manual test scenarios and automate them where appropriate; * Develop and maintain automated UI and API test suites; * Integrate automated tests into the CI/CD pipeline where applicable; * Perform regression testing before releases and support production verification; * Contribute to improving QA processes, testing strategy, and overall product quality.
