MLOps Strategy & Architecture
- Contribute to the definition and evolution of the MLOps strategy for a hybrid cloud environment, ensuring alignment with business objectives, security standards, and industry best practices;
- Support the design of a scalable MLOps platform with a self-service approach, covering the full ML lifecycle, including data ingestion, feature engineering, model training, validation, deployment, and monitoring;
- Develop and maintain clear and structured documentation for MLOps processes, workflows, and infrastructure.
Hybrid ML Lifecycle Implementation
- Design and implement Infrastructure as Code (IaC) solutions using Terraform to provision and manage cloud and on-premises resources;
- Ensure robust security practices to protect sensitive data and ML models across hybrid environments;
- Develop and deploy new platform functionalities while ensuring key non-functional requirements, particularly reproducibility and reliability;
- Build and maintain monitoring dashboards and alerting systems to proactively detect and resolve platform issues.
Collaboration & Contribution
- Collaborate closely with ML engineers, data scientists, software engineers, and infrastructure teams to deliver a scalable and high-quality MLOps platform;
- Communicate effectively with both technical and non-technical stakeholders, ensuring alignment and transparency;
- Stay up to date with emerging MLOps trends, tools, and technologies, actively contributing to continuous improvement;
- Participate in code reviews and contribute to the definition and adoption of best practices.