About the RoleAs a QA Architect, you have a proven track record of defining quality engineering vision, strategy, and architecture for complex digital products and AI-enabled solutions. You have hands-on experience designing, building, and scaling enterprise-grade automated testing frameworks, quality paradigms, and continuous testing ecosystems in a cross-functional environment. You will combine deep software engineering & test automation expertise with a forward-looking approach to AI-driven quality engineering, helping teams deliver reliable, secure, accessible, and high-performing software at scale.
In this role, you will work closely with QA Leads, Solutions Architects, Software Engineers, and Product Executives to establish technical quality standards, QA strategies, test automation & performance frameworks across web, mobile, and cloud environments. As our clients increasingly adopt AI-powered applications, you will also identify opportunities to apply AI-assisted QA, intelligent test orchestration, and agentic workflows to improve test effectiveness, accelerate feedback, and reduce delivery risk.
You bring a strong technical point of view regarding test automation tools, shift-left testing practices, API automation, and infrastructure management for testing. As a senior leader within the QA discipline, you will champion reusable engineering patterns, mentor technical talent, and help evolve Konrad's QA capabilities as software development and AI technologies advance.
Our engineering teams work in smaller groups based on project size, collaborating closely with project management, design, and technology leadership. We strive to create a work environment that encourages innovation, teamwork, career advancement, and personal fulfillment.
What You'll Do- Define quality engineering strategies, architectures, standards, and measurable outcomes across complex digital platforms and client engagements.
- Architect, build, and scale maintainable automated testing solutions across web, mobile, API, integration, and backend systems.
- Drive AI-enabled quality engineering initiatives, including AI-assisted test design, intelligent test selection, automated failure analysis, and agentic testing workflows.
- Evaluate and introduce AI-enabled tools, agents, reusable skills, and orchestration patterns that improve engineering productivity while maintaining reliability, security, and human oversight.
- Establish testing and evaluation strategies for AI-powered applications, including LLM-based systems and AI agents, covering factual correctness, groundedness, robustness, safety, and regression.
- Integrate automated testing, quality gates, static analysis, and risk-based validation into CI/CD pipelines to enable fast, reliable delivery.
- Define approaches to performance, scalability, accessibility, and security testing, collaborating with relevant specialists where appropriate.
- Establish quality metrics and feedback mechanisms to assess test effectiveness, defect escape rates, automation reliability, delivery confidence, and the measurable impact of AI-enabled practices.
- Partner with engineering and architecture leaders to assess system risks, review solution designs, and make informed technical trade-offs.
- Advise clients on Quality Engineering strategy, tooling, operating models, and AI-enabled SDLC transformation.
- Develop reusable frameworks, reference architectures, accelerators, and engineering standards that can be adopted across teams and engagements.
- Mentor QA engineers and technical leads, promote engineering excellence, and help develop the next generation of Quality Engineering talent.
Qualifications- 8+ years of relevant Quality Assurance and Quality Engineering experience, including significant hands-on automation engineering and framework architecture experience.
- Strong software engineering fundamentals and experience designing scalable, maintainable automation frameworks using technologies such as Playwright, Cypress, Selenium, Appium using languages like TypeScript/JavaScript, Python, Java, or equivalent.
- Proven ability to develop quality strategies spanning frontend, APIs, integration, backend services, and distributed systems.
- Strong understanding of CI/CD pipelines, version control, cloud environments, containers, and test execution infrastructure.
Nice to Have- Experience designing agentic workflows, AI-enabled testing systems, or reusable AI engineering capabilities.
- Hands-on experience with LLM evaluation frameworks, RAG testing, model-output assessment, or AI safety and robustness testing.
- Experience with performance engineering, application security testing, accessibility automation, and cloud-native architectures.
- Experience developing internal engineering accelerators, reusable frameworks, or technical offerings for a consulting organization.
Perks and Benefits- Comprehensive Health & Wellness Benefits Package
- Retirement Planning
- Parental Leave Program
- Annual tech & travel allowance
- Flexible Working Hours
- Work from Home Flexibility
- Service Recognition Programs
- Socials, Outings & Retreats
- Culture of Learning & Development
The estimated compensation for this position is $120,000 - $135,000. This is an estimate and a compensation offer will vary based on applicant's education, experience, skills, abilities and alignment with market data.
While we sincerely appreciate all applications, only those candidates selected for an interview will be contacted.
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