MLOps Engineer

Job Summary

The MLOps Engineer is responsible for integrating machine learning models into all environments efficiently and effectively. This role combines elements of machine learning, software engineering, and operations to ensure that models are deployed reliably and can scale to meet demand. MLOps Engineers build, maintain, and optimize machine learning solutions and ensure that ALL algorithms are performing as expected. 

Job Responsibilities 1

Develop and maintain pipelines for deploying machine learning models into our environments by automating the deployment process (CI/CD).

Implement monitoring systems to track the performance and health of deployed models and establish alerting mechanisms to detect any anomalies or performance degradation and make necessary adjustments.

Documentation and reproducibility to ensure that experiments and models are reproducible and well-documented.

Manage machine learning model versioning to ensure smooth transitions between versions. Maintain clear documentation of model configurations, dependencies, and deployment processes to facilitate seamless updates and operations.

Job Responsibilities 2

Optimize models and deployment pipelines to enhance performance and scalability by focusing on efficient load balancing and effective resource management.

Manage the infrastructure for deploying and operating models, whether on the cloud or on-premises. Make sure it's secure, cost-effective, and meets the needs of your machine-learning models.

Oversee data pipelines to ensure high-quality and consistent data for training and inference. Implement practices for data versioning and management

Ensures security in all MLOps processes considering, data protection, deployment security, regulatory compliance, and model integrity.

Additional Responsibilities 3

Job Knowledge & Skills

Comprehensive understanding of the end-to-end machine learning lifecycle, including model training, validation, and deployment.

Knowledge of different model deployment strategies and tools (e.g., REST APIs, model servers)

Understanding of techniques for scaling machine learning models and optimizing performance.

Familiarity with data engineering concepts and tools for handling large datasets and building data pipelines

Knowledge of DevOps practices and how they apply to machine learning workflows.

Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent).

ERP knowledge preferably SAP functional skills is a requirement to be successful in this role

Job Experience

Minimum 5 years working experience, 3 years relevant working experience, 2 years GCC experience is a plus

Competencies

Education