MLOps Platform Engineer — Cloud AI Infra (Hybrid)

Placements24; Vereeniging; Hybrid; ZAR 700,000 - 950,000; Full time

Benefits offered by this job

Competitive salary

Benefits package

Advanced tools access

Hybrid work arrangement

Professional development

Job summary

Placements24 in Vereeniging is seeking an experienced AI/ML Platform Engineer to enhance cutting-edge machine learning infrastructure. You will architect scalable and reliable platforms that empower data scientists to develop and deploy AI models efficiently.

You'll build and automate MLOps pipelines, manage cloud resources, and ensure performance and security of the ML environment, fostering collaboration and robust engineering practices across the team.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Experience in software engineering focusing on infrastructure or platform engineering.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with ML workflows and MLOps best practices.
• Strong programming skills in Python or Go, and experience with infrastructure-as-code tools.

Responsibilities

• Design, build, and maintain scalable and reliable AI/ML infrastructure and platforms.
• Develop and automate MLOps pipelines for model training, deployment, and monitoring.
• Manage cloud-based resources and services for AI/ML workloads (e.g., Kubernetes, Docker).
• Ensure the performance, scalability, and security of the ML platform.
• Collaborate with data scientists and ML engineers to understand their infrastructure needs and provide solutions.

Skills

Python

Go

Infrastructure as code

Education

Bachelor's degree in Computer Science, Engineering, or a related field

Tools

AWS

Azure

GCP

Docker

Kubernetes

Job description

Placements24 in Vereeniging is seeking an experienced AI/ML Platform Engineer to enhance cutting-edge machine learning infrastructure. You will architect scalable and reliable platforms that empower data scientists to develop and deploy AI models efficiently.

You'll build and automate MLOps pipelines, manage cloud resources, and ensure performance and security of the ML environment, fostering collaboration and robust engineering practices across the team.