AI Quality Assurance Architect

CHI Software is a global software development services provider delivering AI-driven solutions, cloud services, data engineering across EdTech, FinTech and HealthTech. We help businesses modernize, scale and unlock new value through the latest technologies.
We are looking for AI Quality Assurance Architect

Requirements

10+ years of experience in software testing, quality engineering, or test architecture.
Proven experience as a Test Architect, Quality Architect, Principal QA Engineer, or Senior Test Lead.
Experience defining test strategies across complex enterprise systems and multiple teams.
Strong knowledge of Agile, DevOps/DevSecOps, CI/CD, and automated quality gates.
Hands-on experience with BDD, Gherkin, Given/When/Then scenarios, and requirements traceability.
Experience integrating automated BDD tests into CI/CD pipelines.
Experience testing AI/LLM or agentic systems, including evaluation frameworks, regression testing, hallucination risks, guardrails, and safety.
Strong knowledge of API, integration, contract, performance, and resilience testing.
Experience with automation languages such as Python, Java, or JavaScript/TypeScript.
Strong stakeholder management and technical leadership skills.

Responsibilities

Own and evolve the end-to-end test and quality architecture.
Define test strategies covering APIs, integration, automation, regression, performance, and production assurance.
Establish automation frameworks, quality gates, and release criteria.
Implement BDD standards and ensure traceability from requirements to automated tests.
Integrate quality practices early into requirements, architecture, and design.
Define AI/LLM testing approaches, evaluation datasets, regression suites, and safety checks.
Ensure coverage of APIs, data flows, third-party integrations, and distributed systems.
Define test-data and environment strategies.
Establish quality metrics, identify systemic risks, and coach QA teams

Will be a plus

Experience in enterprise-scale transformation programmes.
Knowledge of microservices, event-driven architectures, and API-first platforms.
Experience with AWS, Azure, or GCP.
Experience with data-quality testing.
Experience with AI/ML, Generative AI, RAG, or agentic systems.
Experience establishing quality standards across multiple teams or suppliers.
Experience transitioning from manual QA to automated quality engineering.