Lead MLOps Architect – AWS

Experience: 10–12 Years
Location: Egypt (Onsite Role)
Employment Type: Full-Time

Job Summary

We are seeking a highly experienced Lead MLOps Architect with deep AWS expertise to lead the design, architecture, and governance of enterprise-grade ML platforms. This role requires strong leadership capabilities, hands-on expertise in scalable ML systems, and experience managing large production environments.

Key Responsibilities

• Architect and lead enterprise-scale MLOps platforms on AWS
• Define best practices for ML lifecycle management, deployment standards, and governance
• Lead production deployment of ML models using AWS-native services
• Design automated CI/CD pipelines for ML workflows and infrastructure
• Implement advanced monitoring, drift detection, retraining automation, and observability
• Ensure high availability, scalability, security, and cost optimization
• Establish model versioning, reproducibility, and experiment tracking standards
• Lead troubleshooting of complex production issues
• Mentor and lead a team of MLOps and platform engineers
• Collaborate with stakeholders to align ML platform strategy with business objectives

Required Skills & Qualifications

MLOps & Machine Learning

• 10–12 years of overall experience with strong focus on ML production systems
• Proven experience leading ML platform architecture and large-scale deployments
• Deep understanding of ML lifecycle management, governance, and reproducibility
• Hands-on experience with TensorFlow, PyTorch, Scikit-learn
• Strong experience with MLflow or enterprise model management tools

AWS Cloud (Mandatory)

• Advanced hands-on expertise in:
  • Amazon SageMaker (training, pipelines, endpoints)
  • S3, EC2, Lambda
  • ECR, ECS, EKS
  • IAM, CloudWatch
• Experience designing secure, compliant, and scalable ML architectures
• Experience implementing cost optimization strategies on AWS

DevOps, Containers & IaC

• Strong expertise in Docker and Kubernetes (EKS)
• Advanced CI/CD implementation
• Infrastructure as Code using Terraform and/or CloudFormation
• Experience implementing GitOps practices

Programming & Data

• Expert-level Python skills
• Experience designing robust data pipelines
• Strong understanding of SQL/NoSQL systems
• Exposure to streaming or real-time ML systems

Preferred Qualifications

• AWS Professional-level certifications
• Experience with ML security, explainability, and regulatory compliance
• Experience building enterprise feature stores
• Exposure to real-time inference systems

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