AI Platform Engineer - Flexible Hours & ML Infra

Placements24; Vereeniging; Hybrid; ZAR 800,000 - 1,200,000; Full time

Benefits offered by this job

Performance bonus

Health insurance

Professional growth

Flexible hours

PTO

Retirement plan

Job summary

Placements24 in Vereeniging, Gauteng, seeks an AI Platform Engineer to design, build, and scale AI/ML infrastructure. You will support data scientists and ML engineers, ensuring reliable, high-performing platforms for rapid model development and deployment.

The role requires strong software engineering background, distributed systems experience, and proficiency with cloud services (AWS/Azure/GCP), containers (Docker/Kubernetes), and MLOps tools. Hybrid work options may be available.

Qualifications

• Bachelor's degree in CS/Engineering (Master's preferred).
• Experience in software engineering with focus on infrastructure or platform engineering.
• Experience with cloud platforms (AWS, Azure, or GCP) and AI/ML services.

Responsibilities

• Design, build, and maintain scalable AI/ML infrastructure and platforms.
• Develop and optimize data ingestion, processing, and storage pipelines for AI workloads.
• Implement and manage MLOps tools for model training, deployment, and monitoring.
• Ensure performance and efficiency of distributed computing systems for AI tasks.
• Collaborate with data scientists and ML engineers to understand infrastructure needs.

Skills

Python

Distributed systems

MLOps

Cloud familiarity

Education

Bachelor's degree in Computer Science or related field

Master's degree preferred

Tools

Docker

Kubernetes

MLflow

Kubeflow

Spark

Hadoop

Job description

Placements24 in Vereeniging, Gauteng, seeks an AI Platform Engineer to design, build, and scale AI/ML infrastructure. You will support data scientists and ML engineers, ensuring reliable, high-performing platforms for rapid model development and deployment.

The role requires strong software engineering background, distributed systems experience, and proficiency with cloud services (AWS/Azure/GCP), containers (Docker/Kubernetes), and MLOps tools. Hybrid work options may be available.