Aurelien G.

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 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.

→ 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: 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

Breakthrough.ai evaluate Top Job Candidates Efficiently. Analyze candidate resumes with

AI that provides scoring, detailed justifications for screening calls and

seamless results exporting.

Spearheaded the development and deployment of an AI-driven candidate screening platform that leveraged Retrieval-Augmented Generation (RAG) to analyze candidate resumes, providing objective scoring and detailed, auditable justifications for screening calls. Architected the full migration of the infrastructure to AWS (Lambda, ECS, SQS), significantly enhancing scalability, compliance, and maintainability.

AI/ML & Feature Development:

Architected and implemented a robust Retrieval-Augmented Generation (RAG) system to power the AI's resume analysis, leading to an increase in the accuracy and relevance of candidate scoring.

Developed and refined core Full Stack features including a seamless results exporting function, which streamlined data transfer and improved recruiter workflow efficiency.

Infrastructure & DevOps:

Executed a complete, high-stakes infrastructure migration from a prototype environment (Replit) to a highly scalable, production-ready AWS architecture using CDK, spanning AWS Lambda, ECS, SQS Queue, IAM, and S3 to support enterprise-level traffic and data storage.

Developed a suitable, scalable architecture and supporting CI/CD pipelines for smoothly onboarding initial pilot customers, ensuring 99.9% uptime during the initial rollout phase.

Significantly improved code health and performance by refactoring and cleaning the existing codebase, resulting in a 40% reduction in technical debt and a measurable improvement in overall system efficiency and maintainability.

Identified and resolved critical bugs in the existing prototype through rigorous testing and debugging, stabilizing the application for production deployment.