Senior AI/ML Platform Engineer (MLOps, Cloud & Kubernetes)

Placements24; Vereeniging; Ibrido; ZAR 1.200.000 - 1.800.000; Tempo pieno

Vantaggi offerti da questo lavoro

Performance bonus

Health insurance

Hybrid work model

Paid time off

Professional development

Conferences

Descrizione del lavoro

Placements24 is seeking a Senior AI/ML Platform Engineer in Vereeniging, Gauteng to design, build and maintain scalable MLOps platforms that empower data scientists and engineers to train, deploy, and monitor ML models.

Ship robust ML infrastructure with CI/CD, cloud services (AWS/Azure/GCP), and container orchestration using Docker and Kubernetes, while controlling costs and ensuring security.

Competenze

• Bachelor's or Master's in computer science, engineering or related field.
• 5+ years in software engineering or site reliability with ML platforms/MLOps.
• Proven production ML systems experience.
• Strong Python/Go coding skills.
• Experience with AWS/Azure/GCP ML services.
• Kubernetes and Docker expertise; IaC tooling exposure.

Mansioni

• Design, build, and maintain scalable MLOps infrastructure and platforms.
• Automate ML workflows: data pipelines, training, deployment, monitoring.
• Implement CI/CD for ML models and pipelines.
• Manage cloud-based ML environments (AWS/Azure/GCP).
• Work with Docker and Kubernetes for deployment and scaling.
• Collaborate with data scientists to meet infrastructure needs.
• Ensure security, performance, and cost efficiency of the platform.
• Troubleshoot ML infra issues and optimize pipelines.
• Stay updated on MLOps trends and define best practices.

Conoscenze

Python

Go

MLOps

CI/CD

Cloud platforms

Formazione

Bachelor's or Master's in CS/Engineering

Strumenti

Docker

Kubernetes

Terraform

Ansible

Jenkins

Descrizione del lavoro

Placements24 is seeking a Senior AI/ML Platform Engineer in Vereeniging, Gauteng to design, build and maintain scalable MLOps platforms that empower data scientists and engineers to train, deploy, and monitor ML models.

Ship robust ML infrastructure with CI/CD, cloud services (AWS/Azure/GCP), and container orchestration using Docker and Kubernetes, while controlling costs and ensuring security.