MLOps AI Engineer | Hybrid & Cutting-Edge Deployments

Placements24; LegKraal Gate; Hybrid; ZAR 900,000 - 1,300,000; Full time

Benefits offered by this job

Competitive salary

Professional development opportunities

Hybrid work arrangements

Collaborative culture

Work on cutting-edge AI projects

Job summary

Placements24 seeks a skilled AI Engineer with MLOps expertise to design and maintain robust ML deployment workflows. You will build and manage CI/CD pipelines, automate model training and monitoring, and develop scalable MLOps infrastructure.

The role requires collaboration with data scientists and engineers to streamline the ML lifecycle, ensuring reliable, scalable ML deployments within a hybrid work setup.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 2+ years of experience in MLOps/DevOps with ML.
• Proficiency in Python and scripting (Bash).
• Experience with cloud platforms (AWS, Azure, GCP).
• Experience with containers (Docker) and orchestration (Kubernetes).
• Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) and ML platforms.
• Strong CI/CD and infrastructure-as-code understanding.

Responsibilities

• Design, build, and manage CI/CD pipelines for ML models.
• Implement tools for automated model training, testing, deployment, and monitoring.
• Develop and maintain MLOps infrastructure including containers and orchestration.
• Collaborate with data scientists and engineers to streamline ML lifecycle.
• Ensure reliability, scalability, and performance of ML deployment environments.
• Troubleshoot and resolve deployment and operational issues.

Skills

Python

Bash scripting

AWS

Azure

GCP

Docker

Kubernetes

TensorFlow

PyTorch

scikit-learn

CI/CD

IaC

Education

Bachelor's degree in Computer Science or related

Tools

Docker

Kubernetes

Job description

Placements24 seeks a skilled AI Engineer with MLOps expertise to design and maintain robust ML deployment workflows. You will build and manage CI/CD pipelines, automate model training and monitoring, and develop scalable MLOps infrastructure.

The role requires collaboration with data scientists and engineers to streamline the ML lifecycle, ensuring reliable, scalable ML deployments within a hybrid work setup.