Machine Learning Ops Engineer M/F - CDI - Monaco

Job description

The Machine Learning Ops Engineer will primarily act as a bridge between the Data team in the broad sense (Data Engineer, Datascientist, AI Engineer) and the Operations/DevOps team. The main objective will be to industrialise Machine Learning projects, from experimentation through to production deployment, guaranteeing robustness, scalability and reproducibility. This technical role is entirely delivery / software development oriented.

Activities

Industrialisation & Pipelines (CI/CD/CT):

• Designing and industrialising use cases: structuring the project technically, implementing tests, etc.
• Designing and maintaining continuous integration (CI), continuous deployment (CD) and continuous training (CT) pipelines for ML models.
• Automate data workflows (Data Pipelines Airflow) in collaboration with the Data Engineer.
• Guaranteeing the reproducibility of training (versioning of data, code and models).

Infrastructure & Deployment:

• Containerising use cases and models (Docker), orchestrating their deployment (Kubernetes).
• Implementing "Model Serving" strategies (REST API, gRPC, batch processing).

Monitoring & Maintenance:

• Implementing monitoring tools to track the health of models in production.
• Detecting and warning of data drift and model drift.
• Manage the complete lifecycle of models (re-training, decommissioning).

Making results available:

• Be able to develop interfaces to make the results available and make the most of them.

Required qualifications

At least five years' higher education in the fields of computing, data, information systems or software engineering.

Length of experience required

At least 2 years in Data Engineering, ML Engineering, AI Engineering or Data Science.

• Fluency in French (reading, writing and speaking);
• Demonstrate scientific rigour, autonomy, technological curiosity and a strong sense of innovation;
• Solid skills in modelling and data analysis: statistical methods, supervised and unsupervised machine learning, time series, evaluation and interpretability of models, parametric modelling and simulations;
• Master Python and its main libraries: numpy, pandas, scikit-learn, PyTorch, TensorFlow, xgboost, lightgbm, statsmodels ;
• Make effective use of collaborative environments and versioning tools: GitLab, GitHub, VSCode, Jupyter, unit tests;
• Use industrialisation and distributed processing tools: Spark, PySpark, Polars, DBT, Kafka, Trino and database querying (SQL, ;
• Implement MLOps practices: pipeline design, automation, CI/CD, model deployment and monitoring (Airflow, MLflow, FastAPI, Docker, Kubernetes);
• Designing relevant visualisations and exploratory analyses using matplotlib, seaborn, plotly or Tableau ;
• ML Ops tools for monitoring models (e.g. MLFlow, giskard, etc.).

• Reliability and rigour, essential for maintaining a critical pipeline fleet.
• Proactive attitude, able to anticipate risks and performance needs.
• Team spirit
• Autonomy and initiative, with the ability to raise appropriate alarms.
• Clear communication, including on complex technical issues.
• Solution-oriented attitude, with a commitment to continuous improvement.

• Teleworking 2 days a week
• Laptop, laptop bag, portable docking station, headphones provided.

We are committed to diversity, gender equality and the employment of disabled workers.

Only European nationals or holders of a residence permit issued by the Préfecture des Alpes-Maritimes (06) are eligible for employment in the Principality.