AWS MLOps Engineer - Build Scalable ML Pipelines

Amidel; Johannesburg; On-site; ZAR 600,000 - 900,000; Full time

Job summary

Amidel is seeking a skilled AWS MLOps Engineer to design, implement and maintain ML solutions on AWS. You will collaborate with cross‑functional teams to deploy models into client environments while ensuring production readiness and regulatory compliance.

The role requires 5+ years in MLOps and 3+ years AWS experience, with hands‑on ability to build scalable ML pipelines, training, evaluation and monitoring.

Qualifications

• Minimum 5 years in MLOps or related roles.
• 3 years of experience with AWS.
• Bachelor's degree in Computer Science, Engineering, Statistics, or a related field.

Responsibilities

• Architect and Deploy ML Solutions: Design end-to-end ML pipelines on AWS, ensuring scalability, reliability, and security.
• Model Training and Evaluation: Collaborate with data scientists to train and evaluate ML models for accuracy and efficiency.
• Infrastructure Management: Build and manage AWS infrastructure (EC2, S3, and related services) with strong security practices.
• Automation and Orchestration: Implement automation to streamline ML workflows.
• Monitoring and Logging: Develop robust monitoring to track model performance in production.
• Security and Compliance: Ensure data protection and regulatory compliance in ML workflows.
• Collaboration and Documentation: Work with cross-functional teams and document knowledge transfer.

Skills

MLOps

Python

CI/CD

Docker

Kubernetes

SageMaker

Security

Collaboration

Education

Bachelor's degree or higher

Tools

Terraform

CloudFormation

CloudWatch

ELK

Job description

Amidel is seeking a skilled AWS MLOps Engineer to design, implement and maintain ML solutions on AWS. You will collaborate with cross‑functional teams to deploy models into client environments while ensuring production readiness and regulatory compliance.

The role requires 5+ years in MLOps and 3+ years AWS experience, with hands‑on ability to build scalable ML pipelines, training, evaluation and monitoring.