Description

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.

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

Too Good To Go
Machine Learning Engineer
AGRICULTURE
December 2022 - Today (3 years and 6 months)
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
Yokai
MLOps
TECH
August 2022 - Today (3 years and 10 months)
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
Python Docker AWS Serverless AWS Lambda
Sievable
Co-Founder
SOFTWARE PUBLISHING
July 2020 - April 2022 (1 year and 9 months)
Paris, France
Building 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 contributors

Using: PyTorch, FAISS, MySQL, Redis, GraphQL, React, Solana Javascript API, RabbitMQ, Terraform, Docker, Kubernetes, Cloudflare Deployed on Scaleway.

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Master of Science
École normale supérieure Paris-Saclay
2019
Master of Science - MS, Mathematics
Master of Science
Télécom Paris
2018
Master of Science - MS

Romain Z.

Machine Learning Engineer

About Romain

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