MLOPS Engineer

Responsibilities

• Model Deployment, Model Monitoring, Model Retraining
• Deployment pipeline, Inference pipeline, Monitoring pipeline, Retraining pipeline
• Drift Detection, Data Drift, Model Drift
• Experiment Tracking
• MLOps Architecture
• REST API publishing

Job Responsibilities:

· Explore, research, and implement MLOps tools, frameworks, and platforms to support our Data Science initiatives.

· Manage and execute a backlog of initiatives aimed at increasing the organization’s MLOps maturity.

· Drive a modern, agile, and automated operating model for Data Science.

· Deliver internal training sessions and presentations covering the benefits and practical use of MLOps tools.

Required experience and qualifications:

· Extensive experience with Kubernetes.

· Experience operationalizing Data Science projects (MLOps) using at least one widely used framework or platform (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube).

· Strong understanding of ML and AI concepts, with hands-on experience in ML model development.

· Proficiency in Python for both ML development and automation; solid knowledge of Bash and the Unix command line toolkit.

· Experience implementing CI/CD/CT pipelines.

· Experience with cloud platforms—preferably AWS—would be an advantage.