DevOps Engineer (AI/ML)

SUMMARY:
DevOps Engineer with strong MLOps experience to help operationalize AI and Machine Learning models

POSITION INFO:

We're looking for a DevOps Engineer with strong MLOps experience to help operationalize AI and Machine Learning models across the business. You will build and manage the cloud, infrastructure, and pipelines that allow Data Scientists and ML Engineers to deploy models into production safely, reliably, and at scale.

This is a senior technical role where you will work closely with AI Engineers, Data Scientists, Cloud Architects, and business teams. You will also have the opportunity to mentor junior engineers and help shape the future of AI engineering within the organisation.

What You Will Do

• Build and maintain CI/CD pipelines for AI/ML workloads.

• Implement DevOps practices for the full ML lifecycle (experiment → train → deploy → monitor → retrain).

• Deploy machine learning models in AWS, Azure, or GCP.

• Use containerization and orchestration tools such as Docker and Kubernetes.

• Manage AI/ML platforms such as MLflow, Kubeflow, SageMaker, or Vertex AI.

• Monitor models in production and set up alerts for performance, drift, and retraining.

• Troubleshoot issues across infrastructure, applications, and pipelines.

• Provide architectural input and help design scalable AI/ML environments.

• Document processes, build reusable frameworks, and support cross-functional teams.

• Mentor junior engineers and support continuous improvement in the team.

What You'll Need to Succeed

Technical Skills

• Solid experience with DevOps tools: CI/CD, GitOps, Terraform/CloudFormation/Pulumi.

• Strong knowledge of containers and orchestration (Docker, Kubernetes, ECS/EKS/GKE).

• Experience deploying AI/ML solutions in cloud environments.

• Understanding of networking, cloud security, and distributed computing (Spark, Ray, Dask).

• Exposure to MLOps platforms (MLflow, Kubeflow, SageMaker, Vertex AI).

• Ability to troubleshoot complex infrastructure and model deployment issues.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or similar (Master's beneficial but not required).

• Relevant certifications welcome:

  • AWS DevOps Engineer / Solutions Architect

  • AWS Machine Learning

  • Certified Kubernetes Administrator (CKA)

  • CKAD or other Kubernetes credentials

Who This Role Is Ideal For

Someone who:

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