Senior MLOps Engineer

Senior MLOps Engineer (Onsite | Cairo, Egypt)

Experience: 5–8 Years

Employment Type: Full-Time

Location: Cairo, Egypt (Onsite)

Salary Range: 80,000 EGP – 100,000 EGP per month

About The Opportunity

HireOn is recruiting for its reputed international client seeking a highly skilled Senior MLOps Engineer to lead the operationalization of machine learning models in production environments.

This role requires a hands-on expert who can bridge Data Science and Cloud Engineering teams, ensuring scalable, secure, and automated ML systems on AWS infrastructure. The ideal candidate will drive end-to-end MLOps architecture, CI/CD automation, and cloud-native ML deployment strategies.

Core Requirements Experience

• 5–8 years of overall experience with minimum 3+ years in MLOps or production ML environments
• Strong experience managing the full ML lifecycle (training, deployment, monitoring, optimization)
• Proven ability to work independently and collaborate across cross-functional teams
  
  

AWS & Cloud Expertise (Mandatory)

Hands-on Experience With

Amazon SageMaker, S3, EC2, Lambda, IAM, CloudWatch, ECR, ECS, EKS

Strong understanding of secure, scalable, and highly available AWS architecture

MLOps & Machine Learning

• Model deployment and monitoring in production
• Experience with TensorFlow, PyTorch, or Scikit-learn
• Experiment tracking tools such as MLflow
• Model performance monitoring and drift detection
  
  

DevOps & Automation

• Docker and containerization
• CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline
• Infrastructure as Code (Terraform or CloudFormation)
  
  

Programming & Data

• Strong Python programming expertise
• Experience with SQL and working knowledge of NoSQL databases
• Experience handling structured and unstructured datasets
  
  

Key Responsibilities

• Design and implement scalable end-to-end MLOps pipelines
• Deploy and manage ML models using AWS-native services
• Build and maintain CI/CD pipelines for ML workflows
• Implement model monitoring, logging, and performance tracking
• Containerize ML applications and deploy on ECS/EKS
• Automate infrastructure using Terraform or CloudFormation
• Ensure system scalability, reliability, and security
• Troubleshoot ML pipelines and cloud infrastructure issues
• Collaborate closely with Data Science and Engineering teams to productionize ML solutions
  
  

Nice to Have

• Exposure to feature stores and data versioning
• AWS Associate-level certification
• Understanding of ML governance, compliance, and model risk management
  
  

Skills: aws codepipeline,terraform,tensorflow,aws sagemaker,mlops,python,aws,ml