Senior MLOps Engineer - AWS

Valleysoft | Center of Excellence is a regional IT services provider based in Egypt, serving clients globally since 2006. The company collaborates with global partners like Oracle to address diverse business and technical challenges, from enterprise application development to process management. Valleysoft's vendor-neutral and process-oriented approach, coupled with operational maturity, ensures high-quality and cost-effective services for clients.

Job Summary

We are looking for a skilled and hands-on Senior MLOps Engineer with strong AWS expertise to support the deployment, automation, and monitoring of machine learning models in production. The ideal candidate will collaborate closely with Data Science and Engineering teams to operationalize ML models using cloud-native best practices.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines from data ingestion to model deployment.
• Deploy and manage ML models using AWS-native services such as SageMaker.
• Build and maintain CI/CD pipelines for ML workflows.
• Implement model monitoring, performance tracking, and basic drift detection.
• Containerize ML workloads using Docker and deploy on EKS/ECS.
• Support infrastructure automation using Terraform or CloudFormation.
• Ensure scalability, availability, and security of ML systems.
• Collaborate with cross-functional teams to productionize ML solutions.
• Troubleshoot ML pipelines and cloud infrastructure issues.

Requirements

Required Skills & Qualifications

MLOps & Machine Learning

• 5–7 years of overall experience with at least 3+ years in MLOps or ML production environments
• Experience managing ML lifecycle (training, deployment, monitoring)
• Hands-on experience with TensorFlow, PyTorch, or Scikit-learn
• Experience with MLflow or similar experiment tracking tools

AWS Cloud (Mandatory)

• Hands-on experience with:
• Amazon SageMaker
• S3, EC2, Lambda
• IAM, CloudWatch
• ECR, ECS or EKS
• Understanding of secure and scalable AWS architecture

DevOps & Automation

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

Programming & Data

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

Good to Have

• Exposure to feature stores and data versioning
• AWS Associate-level certification
• Basic understanding of ML governance and compliance