Aurelien G.

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.

Medotrax specializes in providing advanced digital solutions for cognitive testing and patient surveys to support clinical research and healthcare in neurology. Their flagship product, BraincheX, is a cloud-based digital testing solution for the early detection and diagnosis of dementia, specifically Alzheimer's disease. The company's tools are designed to be precise, scalable, and accessible, aiming to address the underdiagnosis of dementia. They also offer digital surveys for patient-reported outcomes, and their solutions are clinically validated, gamified, and accessible for users with various difficulties.

- Spearheaded the development of a multi-tenant architecture to support clinical research and healthcare solutions.

- Built a comprehensive dashboard to handle user, site, study, and super admin access for tenant management, including user role-based management.

- Designed and implemented the backend using Typescript, MongoDB, and Python, leveraging AWS services for a secure and robust infrastructure.

- Used Next.js, React-Native, Redux, and TailwindCSS to build a scalable and intuitive platform for cognitive testing and patient surveys.

- Leveraged AWS services including Amplify, Cognito, Lambda, API Gateway, S3, and Secrets Manager to deploy and manage a secure cloud environment.

- Ensured compliance with data privacy standards, including GDPR and ISO IEC 27001.

- Implemented tools like Tanstack and ZOD for data management and validation, and PostHog for product analytics.

- Deployment using Expo EAS, and TestFlight.

- Implemented observability and monitoring.

Technologies used: Typescript, MongoDB, Next.js, React-Native, AWS, Amplify, Cognito, Lambda, API Gateway, Python, Multi-tenant architecture, Expo EAS, Redux, i18n, TailwindCSS, CloudWatch, Tanstack, ZOD, PostHog, GitHub, Atlassian, S3, TestFlight.