À propos de Romain
Machine Learning Engineer with data engineering and analytics skills.
I'm specialised in analytics for unstructured data. I can help you make sense of your unstructured data (user feedback / reviews, CX tickets, surveys, ...) and correlate it with product and business metrics.
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 50 km)
Expériences
- Too Good To GoMachine Learning EngineerAGROALIMENTAIREdécembre 2022 - Aujourd'hui (3 ans et 6 mois)First ML Engineer hired to design and build a discount optimisation system from scratch to help retailers reduce waste and increase profits.Including:- Setting up and maintaining data pipelines (DBT, Snowflake)- Developing ML models to predict discount elasticity- Developing an optimisation system to decide which discount to apply and when- Developing and maintaining ML pipelines (Metaflow)- Working with other teams to improve data quality and availability- Developing a small experimentation framework and running AB tests in stores- Working closely with our PM to improve the product
- YokaiMLOpsHIGH TECHaoût 2022 - Aujourd'hui (3 ans et 10 mois)Paris, France- Python code refactoring- Migration from Flask app to Celery app with Redis backend- Implementation of integration tests (Docker Compose, MinIO, Pytest, Celery, Redis) running in Github worflow- Migration from Kubernetes on Google Cloud Platform to Serverless + S3 + Cloudfront + Terraform on AWS- Implementation of a simple authentication feature using Lambda Authorizer, DynamoDB and Basic auth.Yokai.ai:
- Python code refactoring
- Migration from Flask app to Celery app with Redis backend
- Integration tests (Docker Compose, MinIO, Pytest, Celery,
Redis) running in Github worflow- Migration from Kubernetes on Google Cloud Platform to Serverless
- Simple authentication feature using Lambda Authorizer,
DynamoDB and Basic Auth.- Development of a Slack bot to validate ML generated images
- Deployment of a Triton Inference Server on AWS Sagemaker Multi-Model Endpoint with Terraform and a Gradio interface with the Serverless Framework
- SievableCo-FounderEDITION DE LOGICIELSjuillet 2020 - avril 2022 (1 an et 9 mois)Paris, FranceBuilding a collaborative filter-based search engine powered by machine learning, including:- joined Inria's Startup Studio with 1 year of funding- training of a neural network to predict whether a natural language filter matches an item description or not- design of a scalable search engine system with search result candidates selection using clustering- designed a custom MLOps pipeline to automatically pull our datasets from different sources, train our model, update our items embeddings and deploy everything- developed our own search engine platform allowing users to search, index new content and contribute by annotating data- created our own cryptocurrency on the Solana blockchain to reward contributorsUsing: PyTorch, FAISS, MySQL, Redis, GraphQL, React, Solana Javascript API, RabbitMQ, Terraform, Docker, Kubernetes, Cloudflare Deployed on Scaleway.
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Formations
- Master of ScienceÉcole normale supérieure Paris-Saclay2019Master of Science - MS, Mathematics
- Master of ScienceTélécom Paris2018Master of Science - MS