QA Team Lead — Manual, Automation & AI-Driven Testing (AIQ Platform)
About Kaleidoo & AIQ
Kaleidoo is a leading provider of Big Data and AI solutions. AIQ is our flagship AI platform — combining advanced AI models across documents, audio, and media. AIQ is deployed across various environments and serves large-scale, enterprise customers.
Role Overview
As QA Team Lead for AIQ, you will own quality across a complex, AI-first, multi-modal platform — combining classical software QA discipline with AI quality engineering practices for LLM, RAG, and ML-driven features. You will define how we measure quality for systems whose outputs are probabilistic by design and build the evaluation harnesses, automation frameworks, and processes that enable us to release quickly without regressing on accuracy, safety, or performance.
You will be both strategic and hands-on: leading and mentoring QA engineers; partnering with Product, R&D, AI/ML, and DevOps; and building the automation, eval, and observability layers that scale with a platform handling tens of millions of files
Skills & Qualifications:
10+ years in Software QA, including 5+ years leading or mentoring QA engineers.
Strong hands-on experience in both manual and automation testing.
Proven experience with automation frameworks (Playwright or Cypress; Selenium acceptable; Appium for any mobile work).
Python proficiency. JavaScript/TypeScript is also valuable.
Deep experience with API testing (REST, OpenAPI/Swagger) and contract testing across microservices.
Hands-on experience with CI/CD pipelines (GitHub Actions, GitLab CI, or similar) and embedding tests into release flows.
Kubernetes literacy — ability to debug failing tests against a live cluster (kubectl, logs, port-forwarding, Helm).
Hands-on cloud experience in GCP, Azure, or AWS (GCP strongly preferred; experience across multiple is a strong plus).
Familiarity with modern AI-assisted QA tooling: GitHub Copilot, Claude/ChatGPT for test generation, Cursor; AI-augmented test platforms (Mabl, testRigor, Functionize); visual AI testing (Applitools, Percy).
Strong understanding of test planning, test design (boundary/equivalence, risk-based), and defect lifecycle.
Familiarity with Agile/Scrum.
Strong analytical thinking and ability to reason about probabilistic systems.
Excellent communication and collaboration skills — comfortable working across Product, R&D, AI/ML, and DevOps.
B.Sc. / M.Sc. in Computer Science, Software Engineering, or a related field.
Strong Advantage
Experience testing data-intensive, AI/ML, or LLM-powered systems.
Experience with vector databases.
Experience with performance/load testing tools (k6, Locust, JMeter), including GPU-bound workloads.
Experience validating air-gapped or on-premise deployments for enterprise customers.
Experience with security testing for AI systems.
