A Senior Quality Development Engineer building well beyond traditional quality workflows.

I am a Senior Quality Development Engineer at InterSystems. The title is accurate, but the work is broader than traditional quality engineering: it sits across test infrastructure, automation, product-quality workflows, and AI-assisted engineering systems.

My core role sits between Quality Development and Test Automation Environments. QD owns testing, regression, and product-quality workflows. TAE provides internal infrastructure for VM provisioning, InterSystems IRIS deployment, and environment configuration. TAE infrastructure is not AI infrastructure.

My job is to understand QD requirements, understand the available platform capabilities, and translate ambiguous testing and environment needs into automation workflows that engineers can actually use. That means making design tradeoffs, exposing the right configuration surface, handling operational edge cases, and iterating after adoption.

I own and evolve large-scale regression automation covering 10,000+ tests per run, including platform targeting, environment configurability, failure lifecycle automation, recurrence detection, ticketing workflows, exclusion/re-inclusion governance, and cross-team coordination across testing and infrastructure stakeholders.

In parallel with that formal infrastructure and automation work, I have proactively driven AI-assisted engineering initiatives across Quality Development: evidence-grounded failure investigation over failure artifacts, diagnostic logs, documentation, and test context; codebase impact analysis for ObjectScript-heavy systems; reusable AI and Model Context Protocol (MCP) tooling; and mentoring engineers applying AI to their workflows.

I have also worked with Quality Development leadership on practical AI adoption across the department: identifying high-value engineering use cases, encouraging AI education, and helping turn early AI interest into reusable workflows, tools, and practices.

Across my work, the pattern has been translating ambiguous engineering needs into reliable automation: understanding the workflow, mapping requirements to platform capabilities, making pragmatic design tradeoffs, and owning systems through implementation, adoption, and iteration.