AWS MLOps Engineer – Machine Learning Solutions

About the role

Amidel, a technology and business consulting firm, is looking for an experienced AWS MLOps Engineer to design, implement and maintain machine‑learning solutions on the AWS platform for its clients, including banking sector engagements. The role involves close collaboration with data scientists and cross‑functional teams to operationalise models and ensure they meet security and compliance standards.

Key responsibilities

• Architect and deploy end‑to‑end ML pipelines on AWS, ensuring scalability, reliability and banking‑sector compliance.
• Collaborate with data scientists to train, evaluate and optimise ML models for accuracy and efficiency.
• Build and manage AWS infrastructure (EC2, S3, SageMaker, etc.) following strict security standards.
• Implement automation and orchestration tools to streamline ML workflows.
• Develop monitoring and logging mechanisms (CloudWatch, ELK, Prometheus) to track model performance in production.
• Ensure security best practices and data‑protection compliance throughout the ML lifecycle.

Required profile

• Minimum 5 years of MLOps experience and at least 3 years working with AWS.
• Bachelor’s degree in Computer Science, Engineering, Statistics or a related field.
• Preferred certifications: AWS Certified Machine Learning – Specialty, AWS Certified DevOps Engineer – Professional, or other relevant AWS certifications.

Required skills

• Deep knowledge of AWS services such as SageMaker, Lambda, Glue.
• Hands‑on MLOps expertise including model versioning and CI/CD for ML.
• Proficiency in Python or C# for building ML workflows.
• Experience with Docker containerisation and Kubernetes orchestration.
• Infrastructure‑as‑Code using Terraform or AWS CloudFormation.
• Monitoring and logging tools like CloudWatch, ELK stack, Prometheus.