QA Engineer - GTM Applications (Remote, IND)

As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We’re also a mission-driven company. We cultivate a culture that gives every CrowdStriker both the flexibility and autonomy to own their careers. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you.About the Role:This role is part of CrowdStrike’s Core Tech, Go To Market IT Apps Team — a team of Architects, Engineers, QA, BSAs, and Product Owners delivering highly Reliable, Scalable, and Secure Infrastructure and Automation Services across GTM Applications to Accelerate Business Velocity and Operational Excellence. As part of the GTM AI Pod, every member is expected to embrace Agentic AI technologies, operate with an open-source AI engineering mindset, and actively contribute to building the next generation of intelligent GTM workflows.As the QA Engineer for the GTM AI Pod, you are the quality guardian for a workstream that builds non-deterministic AI systems — and that distinction matters. Traditional QA playbooks were designed for deterministic software; they break down the moment you introduce LLMs, autonomous agents, and probabilistic retrieval pipelines. This role requires you to rethink quality from first principles: designing evaluation frameworks that account for variable outputs, defining what ‘correct’ means for an agentic workflow, and building repeatable test suites that give engineers and stakeholders genuine confidence across every release. You will be embedded in the delivery team from requirements through production, owning the test strategy, automation framework, and quality bar for all workstream deliverables — spanning Salesforce integrations, Slack applications, RAG pipelines, agentic workflows, and the cloud infrastructure that ties them together.What You’ll Do: Define and own the end-to-end test strategy for agentic AI workstreams, establishing quality standards that account for the probabilistic, non-deterministic nature of LLM-powered systems.Design and implement AI-specific evaluation frameworks covering hallucination detection, prompt quality scoring, agent task completion rates, and output faithfulness against ground-truth references.Build and maintain automated test suites in Python using frameworks ( pytest, robot framework etc ) covering unit, integration, and system-level scenarios across all workstream components.Develop RAG pipeline test coverage including retrieval precision and recall, semantic relevance scoring, context faithfulness, and end-to-end query-to-answer accuracy using tools such as RAGAS.Design and Build QA automation tests leveraging industry-standard tools and technologies, encompassing functional, regression, and end-to-end integration testing across connected systems and platforms.Build and execute Slack integration test suites validating bot response correctness, Workflow Builder trigger fidelity, agentic Slack bot state management, and error handling under edge-case inputs.Integrate automated tests into CI/CD pipelines (GitHub Actions, Copado, Jenkins) so every pull request is gated by a defined quality bar before merge.Design and execute performance and load tests for LLM-powered APIs, measuring latency percentiles, token throughput, and degradation patterns under concurrent load.Conduct security and adversarial testing including prompt injection attempts, output validation for sensitive data leakage, and collaboration with the DevSecOps team on SAST/DAST pipeline findings.Develop a regression strategy for non-deterministic outputs, defining acceptable variance thresholds, snapshot-based comparisons, and statistical scoring methods that flag genuine regressions without false positives.Validate observability stack completeness — confirm that distributed tracing, structured logging, SLOs, and AI-specific metrics (latency, token throughput, hallucination rates) are instrumented correctly and alerting as expected.Collaborate with engineers and the AI Product Owner from requirements grooming through sprint review, contributing testability requirements, acceptance criteria, and definition-of-done checklists.Own API contract testing across internal and third-party integrations (Salesforce, Marketo, Snowflake, Gong, Clari, G-Suite) using tools such as Postman or REST-assured.Drive defect lifecycle ownership: triage, severity classification, root cause analysis, regression prevention, and post-release qual