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Aurelien GERIER

Expert & Passionate Fullstack Engineer 11+ yrs exp & Media

$50k+: Earned

5x: Hired

4.90: Rating

78: Followers

Los Angeles, USA

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Los Angeles, USA

Led delivery of a cloud-native K-12 student wellness platform that helps counselors monitor wellbeing, surface at-risk students, and act early—balancing product velocity with FERPA/COPPA-aware design and multi-tenant isolation (API RBAC, PostgreSQL RLS, district-scoped analytics). → Owned technical direction and delivery rhythm across a TypeScript monorepo (pnpm/Turbo): backend API, web app, shared packages, DB migrations, CI/CD gates, and staged deploys to GCP (e.g. Cloud Run), aligning engineering work with roadmap and release risk. → Orchestrated an AI assistant for counselors on Google Vertex AI (Gemini): system prompts, safety settings for K-12, function-calling tool design, multi-round tool loops, and strict separation so UI widgets render from verified tool results—not model hallucinations—with tracing/feedback hooks (e.g. Langfuse) for quality and auditability. → Drove access-control and compliance posture for sensitive student data: role/permission middleware, tenant-scoped queries, and defense-in-depth database policies—documentation and evidence suitable for enterprise security reviews (e.g. SOC 2–style narratives). → Partnered on product and program management: phased feature rollout, PR/branch discipline, and clear ownership of scope vs. risk so pilot schools could onboard without compromising data boundaries. → Shaped integrations and platform boundaries: Firebase auth, Neon PostgreSQL + Drizzle ORM, real-time patterns, BigQuery analytics isolation, and operational practices (testing, lint, type-check) that keep the stack maintainable at scale. → Established a disciplined, AI-accelerated delivery model—not ad-hoc prompting: Cursor rules and Claude skills/agents for repeatable workflows; Husky + lint/type-check/test gates; GitHub Actions CI/CD; Cursor Cloud Agents for automated PR review; and daily monitoring of E2E, coverage, and critical issues—owning AI-orchestrated implementation while enforcing accuracy, consistency, and observability