- Bâtiment / Travaux publics, Intelligence artificielle / Machine Learning, SaaS / Cloud Services
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The tech team vision is to build a high-class digital platform for the construction industry that addresses sustainability challenges by leveraging construction material knowledge.
The mission goals include creating and implementing innovative solutions that reduce their carbon footprint, increase industrial productivity and efficiency by combining cloud-native cutting-edge Technology, Data enriched through Machine Learning, and DevOps methodology.
Respect, curiosity, inventiveness, adaptability, and a dedication to excellent quality are shared values of our team.
The team's capabilities include knowledge in cloud computing, software development, data engineering, data science, and DevOps.
move to production every week
Technologies and tools
At the heart of the ORIS, the data pipelines leverages Data Science, Data Engineering and Data Analysis disciplines to built a high quality material information datasets. This takes advantages of AWS SageMaker, Serverless functions with Python and other data services on AWS.
Our cloud-only strategy is reflected in a highly flexible cloud platform on AWS using its managed services and serverless services to increase the developer experience and reduce management effort to focus on innovation.
The ORIS Saas Platform is composed of multiple microservices and microfronteds using Java Spring Boot, Python, React and GraphQL.
Organization and methodologies
Multiple value stream teams within ORIS are in charge of assisting various business units in attaining their objectives and satisfying their clients. Kanban is being utilized by all teams to organize the work items and guarantee flow. To facilitate speedy communication, the team does daily standups. We highlight the week's progress in the weekly review with demos and get business acceptance and sign-off, triggering the weekly move to production. A joint platform meeting is scheduled to discuss the overall strategy, successes, modifications, and objectives for the upcoming time frame. Most of the communication takes place on JIRA and Slack.
Projects and tech challenges
The Data Pipeline implementation takes advantage of machine learning models on geospatial data. We use SageMaker and models to identify construction material production sites, and classify them. The generated dataset is then enriched with data from multiple sources. As the location of sites can highly impacts the transportation cost and CO2 footprint, increasing the precision and exhaustivity is a key challenge. This project enables faster deployment of ORIS and better quality of our product.
ORIS application started with a traditional monolithic architecture. This architecture comes with advantages but also constraints like a lack of flexibility, scalability and a low development speed. As we continuously improve our application architecture, lately the strategy has been set to decompose our existing application into even smaller microservices and microfrontends. This new architecture brings multiple improvements : acceleration of the time to market, faster root cause analysis and resolution of bugs, higher flexibility and scalability, better performances and increased focus within the development teams.
We are constantly and rapidly growing, which is why our recruitment process is short and reactive for a secure hiring as soon as possible:
- Pre-qualification by phone with the manager or the HR department
- 2 interviews: one with the manager and an expert, the other with the HR department and one of the managers
- Negative answer by email within 5 days. Positive response by phone and email within 5 days
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