Boot Sequence
> init profile --candidate "Platform Engineer"
> set specialization "Platform Engineering, AI Infrastructure"
> status: READY_FOR_NEXT_CHALLENGE
Platform Engineer with 5+ years of experience building internal platforms, developer tooling, and AI-powered systems at scale. Proven track record in designing platform abstractions that enable rapid experimentation, improving system reliability through observability, and delivering production-grade services on GCP using Kubernetes and Terraform. Currently focused on platform engineering and AI infrastructure, including LLM evaluation, inference workflows, and developer productivity tooling.
Problem: Teams depended on manual platform workflows that slowed delivery and experimentation.
Architecture: Internal platform abstractions for self-service configuration, release orchestration, and content delivery.
Impact: Reduced deployment lead time from days to hours and enabled faster iteration across product teams.
Problem: Experiment velocity was limited by fragmented tooling and high setup overhead.
Architecture: Reusable platform layers for experiment configuration, rollout pathways, and measurement integration.
Impact: Supported 50+ experiments per quarter and contributed to ~1% conversion uplift through optimisation.
Problem: AI feature quality and reliability needed consistent, automated evaluation standards.
Architecture: Internal LLM benchmarking tooling plus reusable TypeScript SDK scoring pipelines and QA workflows.
Impact: Delivered production LLM-powered Q&A features and open-sourced an SDK for repeatable quality validation.
Problem: Distributed services required stronger reliability, tracing, and incident response under scale.
Architecture: Kubernetes + Terraform on GCP with OpenTelemetry instrumentation, monitoring, alerting, and error tracking.
Impact: Improved reliability through proactive issue resolution and delivered critical payment integration used by 26% of new bookings.