technologies-expected :

responsibilities :

Design, build, and maintain the infrastructure for deploying and scaling machine learning models.
Develop automated CI/CD pipelines for ML model training, testing, and deployment.
Monitor model performance, stability, and system health in production environments.
Collaborate with data scientists and engineers to streamline the transition from model development to production.
Implement tools for model versioning, governance, and observability.
Ensure data security, compliance, and reproducibility of models.
Help shape our overall MLOps strategy and architecture for sustainability and scalability.

requirements-expected :

5+ years of experience in MLOps, DevOps, or similar roles.
Hands-on experience with cloud platforms, especially Microsoft Azure (Azure Functions, Azure ML, Azure Container Apps).
Deep experience with containerization and orchestration (Docker, Kubernetes).
Proficiency with Databricks for developing and operationalizing machine learning workflows.
Familiarity with ML platforms and tools like MLflow, SageMaker, Kubeflow, or similar.
Experience with data pipelines, workflow orchestration (e.g., Airflow), and monitoring tools (e.g., Prometheus, Grafana).
Solid understanding of the full ML lifecycle – from data ingestion to model deployment and monitoring.
Programming experience in Python and infrastructure-as-code tools (e.g., Terraform, CloudFormation).
Strong communication skills and a proactive mindset for working in cross-functional teams.

offered :

A flexible, independent work environment with the freedom to shape your own workflows.
Exciting projects in recruiting technology, working with innovative external teams.
Opportunities for continuous learning and professional growth.
Competitive compensation based on expertise and project scope.
The position is available in both full-time and part-time, offering flexibility to match your preferences and availability.

benefits :

responsibilities :