- Company Name
- MP DATA
- Job Title
- Machine Learning Engineer – Industrialisation des modèles (H/F)
- Job Description
-
**Job Title:** Machine Learning Engineer – Model Industrialization (MLOps)
**Role Summary:**
Design, develop, and operationalize machine‑learning models into scalable, production‑grade services. Build and optimise data ingestion, feature engineering, and training pipelines; implement CI/CD, containerisation, and observability solutions for ML workflows. Collaborate with data scientists, data engineers, and IT teams to translate proof‑of‑concepts into robust, cloud‑native deployments.
**Expectations:**
* Minimum 3 years’ professional experience in ML Engineering, MLOps, or ML‑oriented Data Engineering.
* Proven ability to move prototypes into reliable, production‑ready systems.
* Strong ownership and product mindset, delivering end‑to‑end solutions autonomously.
**Key Responsibilities:**
* Develop and maintain data pipelines and training workflows for large‑scale ML projects.
* Implement CI/CD pipelines (GitOps, automated tests, model versioning) for continuous model delivery.
* Containerise models using Docker; orchestrate deployments with Kubernetes and workflow tools such as Airflow or Kubeflow.
* Deploy models as micro‑services (RESTful APIs) on cloud platforms; monitor performance, latency, and drift.
* Ensure model observability: logging, metrics, alerting, and rollback mechanisms.
* Work closely with data scientists to productionize feature stores and data platforms.
* Continuously improve deployment architecture for scalability, reliability, and cost efficiency.
**Required Skills:**
* Programming: Python (scikit‑learn, TensorFlow, PyTorch).
* MLOps tooling: CI/CD pipelines, Docker, Kubernetes, Airflow/Kubeflow.
* Cloud & Data Platforms: AWS/GCP/Azure, data lakes, warehouses (Snowflake preferred).
* DevOps/DataOps: version control, infrastructure‑as‑code, monitoring, observability.
* Model versioning, lineage, and auditability.
* Strong problem‑solving, communication, and collaboration abilities.
**Required Education & Certifications:**
* Engineer‑level degree (Bachelor/Master) in Computer Science, Engineering, or related field.
* Certifications in cloud platforms (AWS, Azure, GCP) or MLOps (e.g., MLflow, TensorFlow Extended) are advantageous.