AI/ML Platform Engineer

Placements24; Randburg; Hybrid; ZAR 900,000 - 1,500,000; Full time

Benefits offered by this job

Competitive salary

Fully remote

AI infrastructure projects

Professional development budget

Access to cutting-edge technologies

Collaborative remote team culture

Impact on AI capabilities

Job summary

Placements24 is seeking an experienced AI/ML Platform Engineer for a fully remote role. You will design and maintain scalable AI/ML infrastructure, implement MLOps, and optimize cost and security across AWS, Azure, and GCP. Strong software, cloud, and IaC skills required.

You'll work with data scientists and ML engineers to streamline the ML lifecycle and enable rapid experimentation to production at scale, from anywhere in the world.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in software engineering, DevOps, or cloud infrastructure with a focus on ML platforms.
• Proficiency in Python, Go, or Java.

Responsibilities

• Design, build, and maintain scalable AI/ML infrastructure and platforms.
• Implement and manage MLOps best practices, including CI/CD, monitoring, and automation.
• Optimize infrastructure for performance, cost, and security.
• Develop tools and frameworks to streamline the ML lifecycle from experimentation to production.
• Collaborate with data scientists and ML engineers to understand infrastructure needs.
• Manage cloud resources and services (AWS, Azure, GCP) for AI/ML workloads.

Skills

Python

Go

Java

DevOps

Cloud infrastructure

Education

Bachelor's degree in CS/Engineering or related field

Tools

Docker

Kubernetes

MLflow

Kubeflow

Seldon

Terraform

Ansible

AWS

Azure

GCP

Job description

About the Role

Our client, a fast-paced technology company, is seeking an experienced AI/ML Platform Engineer to join their remote team. This role is critical for building, scaling, and maintaining the infrastructure that powers our client's advanced AI and machine learning capabilities. You will be responsible for developing and managing robust ML platforms, ensuring they are efficient, reliable, and accessible to data scientists and engineers. The ideal candidate has a strong background in software engineering, DevOps, and cloud infrastructure, with a passion for enabling cutting‑edge AI development. This fully remote position offers a unique opportunity to significantly impact the core infrastructure supporting AI innovation across the company.

Key Responsibilities

• Design, build, and maintain scalable and reliable AI/ML infrastructure and platforms.
• Implement and manage MLOps best practices, including CI/CD, monitoring, and automation.
• Optimize infrastructure for performance, cost, and security.
• Develop tools and frameworks to streamline the ML lifecycle, from experimentation to production.
• Collaborate with data scientists and ML engineers to understand their infrastructure needs.
• Manage cloud resources and services (e.g., AWS, Azure, GCP) for AI/ML workloads.
• Troubleshoot and resolve issues related to the AI/ML platform.
• Stay current with emerging technologies in cloud computing, DevOps, and MLOps.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in software engineering, DevOps, or cloud infrastructure, with a focus on ML platforms.
• Proficiency in programming languages such as Python, Go, or Java.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Experience with MLOps tools and workflows (e.g., MLflow, Kubeflow, Seldon).
• Strong understanding of infrastructure as code (IaC) principles (e.g., Terraform, Ansible).
• Excellent problem-solving and debugging skills.
• Strong communication and collaboration skills, especially in a remote setting.
• Prior experience working in Randburg, Gauteng or similar tech hubs is a plus.

Benefits

• Highly competitive salary and benefits package.
• Fully remote work arrangement with flexibility.
• Opportunity to work on critical AI infrastructure projects.
• Professional development budget for training and conferences.
• Access to cutting-edge technologies and tools.
• Collaborative and innovative remote team culture.
• Significant impact on the company's AI capabilities.