As Quality Engineering Architect – Data & AI Enablement, you are the anchor point for Quality Engineering within data and AI initiatives. You bridge architecture and implementation, transforming "best practices" into concrete, reusable patterns that teams can adopt.
Your role includes:
- Designing Quality Engineering and AI assurance standards for data and AI solutions (quality gates, test strategies, assurance patterns)
- Defining and implementing CI/CD integrations for reliable releases of data pipelines, models, and agentic AI capabilities
- Defining evaluation, non-regression, and release criteria for models, prompts, and agentic AI flows
- Establishing non-regression and evaluation approaches (output quality, consistency, reproducibility, traceability)
- Advising on testability, observability, and SLO-driven operations (monitoring, logging, audit trails)
- Collaborating with platform, data, and AI teams on paved roads / golden paths and reusable templates
- Coaching engineers and teams: enablement-first approach
- Contributing to presales and bid processes: positioning, approach, deliverables, and quality
You work in a multidisciplinary team where Data, AI, platform engineering, and Quality Engineering converge, with a focus on standardization and sustainable adoption.