MLOps & AI Platform Engineer

About the role

We are looking for an experienced MLOps & AI Platform Engineer to design, build, and operate scalable machine‑learning platforms. You will enable data scientists and AI engineers to develop, deploy, monitor, and maintain models efficiently in a production environment.

Key responsibilities

• Design, build, and maintain enterprise‑grade MLOps platforms and AI infrastructure.
• Develop and automate end‑to‑end ML pipelines for training, validation, deployment, and monitoring.
• Implement model versioning, experiment tracking, and model registry solutions.
• Build scalable CI/CD pipelines for AI/ML workloads.
• Deploy and manage machine‑learning workloads on Kubernetes‑based environments.
• Collaborate with data scientists, AI engineers, data engineers, and DevOps teams to operationalize ML solutions.
• Implement Infrastructure as Code (IaC) for cloud‑native AI platforms.
• Monitor platform health, model performance, and infrastructure availability.
• Ensure platform security, scalability, reliability, and operational excellence.
• Troubleshoot production issues and continuously optimise platform performance.

Required profile

• Bachelor's degree in Computer Science, Software Engineering, AI, IT or a related field.
• 3–11 years of professional experience in MLOps, DevOps, Platform Engineering, Cloud Engineering or AI Infrastructure.
• Strong scripting and automation skills using Python, Bash or similar languages.
• Excellent analytical and problem‑solving abilities.
• Experience working in Agile/Scrum environments.

Required skills

• Kubeflow, Vertex AI Pipelines, SageMaker Pipelines
• MLflow for experiment tracking and model registry
• Apache Airflow for workflow orchestration
• Kubernetes (GKE, AKS, EKS)
• Terraform for Infrastructure as Code
• GitHub Actions for CI/CD automation
• Prometheus for monitoring and observability
• Docker for containerisation
• Cloud platforms: AWS, Microsoft Azure
• Python, Bash scripting