Aurélien Mazoyer, Developer in Paris, France
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Aurélien Mazoyer

Verified Expert  in Engineering

Bio

Aurélien is a senior software engineer who has implemented large-scale systems for big technology companies, such as Tomtom, Amadeus, and Blablacar. As a search engine specialist, he has designed tailor-made solutions leveraging open-source search technologies. At Blablacar, he created a production-ready pricing service leveraging a machine learning model (XGBoost) to predict prices, set up CI/CD with regression testing for service rollout, and developed new features with data scientists.

Portfolio

BlaBlaCar
Python, Flask, Google Cloud Platform (GCP), XGBoost
Amadeus
OpenShift, MongoDB, Kafka Streams, Apache Kafka, Java
TomTom
Apache Lucene, Java

Experience

  • Java - 11 years
  • Solr - 7 years
  • Spring Boot - 5 years
  • Elasticsearch - 5 years
  • Google Cloud Platform (GCP) - 1 year
  • MongoDB - 1 year
  • Apache Kafka - 1 year
  • Python - 1 year

Availability

Part-time

Preferred Environment

Apache Kafka, MongoDB, Elasticsearch, Spring Boot, Java, Python

The most amazing...

...improvement to search performance on TB of data was thanks to a new data model, a migration to Solr, and the development of custom plugins.

Work Experience

Machine Learning Engineer

2020 - 2021
BlaBlaCar
  • Created a production-ready pricing service leveraging a machine learning model (XGBoost) to predict prices. The service is in Python and Flask.
  • Investigated and fixed latency and stability issues to ensure high availability and performance under load.
  • Set up CI/CD with different regression testing phases for the rollout of the service.
  • Developed new features in collaboration with data scientists.
  • Set up a monitoring dashboard and alerting on the service.
Technologies: Python, Flask, Google Cloud Platform (GCP), XGBoost

Senior Data Engineer

2020 - 2020
Amadeus
  • Developed features for a mediation platform for billing based on a micro-service architecture (an Openshift and Kubernetes platform) and a Kafka backbone. The project involves Java, Spring, Kafka Streams for data processing, and MongoDB for storage.
  • Refactored the core API that handles requests to deploy different microservices.
  • Conducted performance testing of the solution (mainly on Kafka Streams) and solved related issues.
Technologies: OpenShift, MongoDB, Kafka Streams, Apache Kafka, Java

Senior Software Engineer

2018 - 2019
TomTom
  • Developed search-related functionalities of the search and geocoding API of Tomtom, that leverages a Java and Lucene back end.
  • Created Kibana dashboards for monitoring search quality metrics of the search and geocoding API.
  • Tuned relevancy of the search engine for different geocoding scenarios.
Technologies: Apache Lucene, Java

Senior Search Engine Engineer

2016 - 2018
Questel
  • Oversaw the technical feasibility study of the migration of the data analytic tool and search engine on patents from a proprietary system, (QP) to Elasticsearch 5.
  • Assisted a team of developers as the Elasticsearch expert and was involved in the development of the Spring service layer leveraging Elasticsearch.
  • Developed Elastic plugins, for example, custom analyzers.
Technologies: Java, Spring, Elasticsearch

Search Engineer and Co-founder

2011 - 2018
FranceLabs
  • Oversaw the architecture of Datafari, an intranet search solution based on Solr. I was also part of the product's development team.
  • Involved in different R&D activities for the company (for example, development of a prototype that integrates machine learning features using Apache Spark).
  • Provided short-term consulting missions on open-source search engine and related technologies (performance and architecture auditing of the search engine and functional and relevancy auditing of the search engine for eCommerce companies such as TheFork.
  • Delivered training on Lucene and Solr as a Lucidworks certified instructor.
Technologies: Cassandra, Apache Spark, Solr, Java

Senior Search Engine Engineer

2014 - 2016
Questel
  • Spearheaded the migration of a Lucene 2.0-based internal search engine (TB of data indexed) to Solr 4.
  • Realized a technical audit and feasibility study of the migration.
  • Implemented different Solr plugins such as a fast query time join plugin, custom query parser, and custom analyzers.
  • Managed the integration in the back end and migration of customer data.
Technologies: Spring, Java, Solr

Experience

Migration of Data Analytic Tool and Search Engine From Proprietary System to Elasticsearch

I was the search expert of the team overseeing the migration of the data analytic tool and search engine on patents from a proprietary system, (QP) to Elasticsearch 5. This system was the core project of the company and was providing services on TB of data through a REST API. I was in charge of the feasibility study and architecture of the project, plugin development (i.e., custom aggregation plugin, custom analyzers) in Elasticsearch, and served as a part of the team of developers implementing a Java and Spring service layer.

Education

2009 - 2011

Engineering Degree in Computer Science

Polytech'Nice-Sophia - Sophia-Antipolis

Certifications

DECEMBER 2016 - PRESENT

Machine Learning

Coursera

Skills

Libraries/APIs

Apache Lucene, XGBoost, REST APIs

Tools

Solr, Kafka Streams, Elastic

Storage

Elasticsearch, MongoDB, Cassandra

Languages

Java, Python, SQL

Frameworks

Spring Boot, Spark, Flask, Spring, Apache Spark

Platforms

Apache Kafka, OpenShift, Google Cloud Platform (GCP)

Other

Data Engineering, Machine Learning, Computer, Programming

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