Senior AI Engineer: Cloud & MLOps Architect

Placements24; Randburg; Hybrid; ZAR 1,200,000 - 1,600,000; Full time

Benefits offered by this job

Health insurance

Professional development opportunities

Retirement plan

Generous vacation policy

Job summary

Placements24 in Randburg is seeking a Senior AI Engineer to design, build, and deploy scalable AI/ML solutions on major cloud platforms. You will architect robust MLOps pipelines, ensuring efficient model deployment, monitoring, and lifecycle management.

The role involves collaborating with data scientists and engineers to operationalize ML models, optimizing AI workloads for cost and performance, and implementing IaC/CI/CD practices for reliable AI deployments in a dynamic cloud environment.

Qualifications

• Bachelor’s or Master’s in CS/Engineering or related field.
• 5+ years in software engineering with AI/ML and cloud tech.
• Proficiency in Python and TensorFlow or PyTorch.
• Experience with Docker, Kubernetes and MLOps.
• Experience with IaC and CI/CD for AI deployments.

Responsibilities

• Design, develop, and implement scalable AI/ML solutions on cloud platforms.
• Build and maintain MLOps pipelines for training, deployment, monitoring, and management.
• Collaborate with data scientists and engineers to operationalize models.
• Optimize AI workloads for performance, cost, and scalability in cloud environments.
• Implement IaC and CI/CD practices for AI deployments.
• Troubleshoot and resolve AI infrastructure and deployed model issues.

Skills

Python

AI/ML

TensorFlow

PyTorch

MLOps

CI/CD

Education

Bachelor's degree in CS/Engineering or related field

Tools

Docker

Kubernetes

AWS

Azure

GCP

Terraform

Job description

Placements24 in Randburg is seeking a Senior AI Engineer to design, build, and deploy scalable AI/ML solutions on major cloud platforms. You will architect robust MLOps pipelines, ensuring efficient model deployment, monitoring, and lifecycle management.

The role involves collaborating with data scientists and engineers to operationalize ML models, optimizing AI workloads for cost and performance, and implementing IaC/CI/CD practices for reliable AI deployments in a dynamic cloud environment.