MLOps Engineer

About the role

Digifloat is looking for an MLOps Engineer in Abu Dhabi to design, build and operate secure, scalable and governed machine learning operations. The role focuses on production-grade ML platforms, automated model deployment pipelines and cloud-based AI infrastructure, with a primary emphasis on AWS. It works closely with Data Scientists, Data Engineers, Platform Engineering, Information Security, Architecture and business stakeholders.

Key responsibilities

• Design scalable MLOps architectures and reusable deployment patterns across development, testing, staging and production, and set enterprise standards for deployment, automation, monitoring and operations.
• Build and maintain automated ML workflows covering data preparation, experimentation, training, validation, approval, deployment, monitoring and retraining, including model registries and experiment tracking.
• Implement CI/CD pipelines and Infrastructure as Code (Terraform, AWS CloudFormation or similar) and automate testing, deployment and release management.
• Deploy batch and real-time models using containerized, serverless and Kubernetes-based architectures, with version control, rollbacks, high availability and scalability.
• Set up monitoring and observability for model performance, data quality and drift, service reliability, infrastructure utilization and latency, including dashboards, alerts, incident response and runbooks.
• Manage AWS services such as SageMaker, S3, ECR, ECS/EKS, Lambda, Step Functions, CloudWatch, IAM, KMS and Secrets Manager, applying cloud security best practices.
• Integrate Dataiku with enterprise datasets and AWS, supporting governed deployment and model promotion.
• Ensure compliance with data privacy, security, Responsible AI and model governance, and maintain model lineage, audit trails and documentation.
• Optimize infrastructure performance and cost through FinOps practices such as resource tagging, utilization monitoring and capacity planning.
• Partner with Data Scientists and ML engineers, support Data Engineering and Platform teams, review code and share MLOps best practices.

Required profile

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Technology, Engineering or a related field; Master's degree preferred.
• Minimum 7+ years of experience in MLOps, ML engineering, DevOps, platform or cloud engineering.
• Proven experience deploying and managing machine learning solutions in production.

Required skills

• Strong hands-on experience with AWS and Amazon SageMaker
• CI/CD pipelines and Infrastructure as Code (Terraform, AWS CloudFormation)
• Model serving on Kubernetes and serverless platforms
• Dataiku integration

What we offer

• Gratuity
• Annual leaves
• Health insurance
• Annual air ticket
• Visa sponsorship