Data Engineer

Since 2014, Wiremind has positioned itself as a **technical company** transforming the world of transport and events with a 360° approach combining **UX, software, and AI**. Dynamic and ambitious, we strive to maintain our technical DNA which is the engine of our success. The company, profitable and self-financed since its creation 10 years ago, is mainly composed **of engineers and experts** and currently supports the growth of our **business model** based on "software-as-a-service" solutions. **Your missions**: At Wiremind, the **Data Science team** is responsible for the development, monitoring and evolution of all ML-powered forecasting and optimization algorithms in use in our Revenue Management systems. Our algorithms are divided in 2 parts: - A **modelling of the demand** using ML models (_e.g._ deep learning, boosted trees) trained on historical data in the form of time-series - **Constrained optimizations problems** solved using linear programming techniques You will be joining a team shaped to have all profiles necessary to constitute an autonomous department (DevOps, software and data engineering, data science, AIML, operational research) and will work on a modern MLOps stack composed of Druid (data warehouse), argo-workflow (pipelines orchestrator), MLFlow (models & experiments tracking) and in-house python packages to glue these components together. As an **MLOps Engineer**, you will be responsible for: - ** Maintaining** the existing MLOps platform used by our ML engineers to train and deploy models - **Improving** the MLOps platform with new features such as **model automatic retraining**: - **Deploying ML models** in production in a safe, scalable and maintainable way - **Exchanging on a daily basis with our ML team** to share ideas about how to improve our solution and **provide technical support** on the stack - **Tackling technical debt,** propose new solutions and challenge architecture decisions to keep on improving the code base **Technical stack** - ** Backend**: Python 3.11+ with SQLAlchemy, Remoulade, FastAPI - **Kubernetes** for infrastructure - **Orchestration**: Argo workflows over an auto-scaled Kubernetes cluster - **Database**: Postgresql, Redis, Druid - **Model versioning and registry tool**: MLFlow - **Common ML libraries/tools**: pytorch, LightGBM, pandas, Dask, Dash, Jupyter - **Gitlab** for continuous delivery - **Prometheus/Grafana and Kibana** for operations **Your profile**: - You have an Engineering degree and 3 to 5 years experience in either MLOps, Software engineering, Data engineering or a related field - You are proficient in a backend programming language and have experience collaborating on large code bases - You are rigorous and committed to deliver high quality, thoroughly tested code - You excel at debugging across multiple layers of architecture **Our benefits**: By joining us, you will integrate: - A self-financed startup with a strong technical identity - Beautiful 700 m² offices in the heart of Paris (Bd Poissonnière) - Attractive remuneration indexed on performance - A caring and stimulating team that encourages skills development through initiative and autonomy - A learning environment with opportunities for evolution ‍ You will also benefit from: - Training on demand - A hybrid policy: 2 days of remote work per week and the possibility to work occasionally from abroad - A great company culture (monthly afterworks, regular meetings on technology and products, annual off-site seminars, team-building ) - An annual budget for your IT equipment - A partnership with the People & Baby network of inter-company nurseries to help with childcare for children aged 0 to 3 **Our Recruitment Process**: - A screening interview with Anne-Laure, our Senior Talent Manager - An interview with Constant, the Hiring Manager - A technical test or case study to be prepared - An interview at our offices to discuss your technical test or case study and meet with members of the team - A culture fit with Charles our CTO and co-founder