Senior MLOps Engineer

MLOps Engineer

Location: Cairo, Egypt(On-Site, Work From Office)

Experience Level: Senior (7–8+ years)

Employment Type: Contract

Department: AI/ML Engineering

About The Role

This role focuses on automating, operationalizing, and managing the end-to-end machine learning lifecycle—from model training and evaluation to deployment and monitoring. You will work closely with Data Scientists, DevOps, and Platform teams to ensure reliable delivery, governance, and scaling of ML systems in production environments.

Key Responsibilities

Mlops & Lifecycle Management

• Design and implement end-to-end MLOps pipelines for ML model lifecycle.
• Automate model training, validation, deployment, and monitoring workflows.
• Manage model registries, feature stores, and version control to ensure reproducibility and lineage.
• Implement strategies for continuous training (CT) and continuous deployment (CD) of ML models.

Ci/Cd & Infrastructure

• Develop and maintain CI/CD pipelines using Azure DevOps, Git, Azure Pipelines, and DVC.
• Integrate automated testing and code quality checks using pytest, behave, SonarQube.
• Support deployment strategies such as Blue-Green, Canary, and Shadow deployments.

Monitoring & Feedback

• Set up monitoring and alerting using Prometheus, Grafana, and other observability tools to track model health and performance.
• Enable feedback loops and auto-retraining in response to concept/data drift.
• Implement rollback and recovery mechanisms for deployed models.

Governance & Responsible Ai

• Establish and maintain model governance frameworks including audit trails, access controls, and compliance policies.
• Promote and implement Responsible AI practices, aligned with FATE (Fairness, Accountability, Transparency, Ethics) principles.

Qualifications & Experience:

• CI/CD & Version Control: Git, Azure DevOps, Azure Pipelines, DVC
• Experiment Tracking & Registries: MLFlow, Azure ML
• Testing & Quality: pytest, behave, SonarQube
• Orchestration: Airflow, (Azure Data Factory – optional)
• Monitoring: Prometheus, Grafana, Email Notifications
• Deployment: Docker, (Kubernetes – optional), Azure ML Endpoints
• Programming Languages: Python, Bash, YAML, JSON
• Storage & Compute: Azure Blob, HDFS, Cloudera

Preferred Qualifications

• 7–8+ years in MLOps, ML Engineering, or DevOps with a machine learning focus.
• Proven hands-on experience deploying ML models at scale in production environments.
• Strong expertise in model monitoring, drift detection, and automated retraining.
• Familiarity with Responsible AI frameworks and ethical AI best practices.