AI/ML Systems Engineer: Build & Deploy Scalable ML

Placements24; Soweto; Vor Ort; ZAR 900.000 - 1.200.000; Vollzeit

Benefits dieser Stelle

Health insurance

Retirement savings options

Training resources and conferences

Zusammenfassung

Placements24 is seeking an experienced AI/ML Systems Engineer to design, build, optimize, and deploy robust AI/ML systems in Soweto, Gauteng. You will collaborate with data scientists, software engineers, and researchers to ensure high-performance production environments and seamless integration with core products and services.

The role emphasizes building scalable pipelines, automating ML workflows, and applying MLOps best practices, including Docker and Kubernetes, across cloud platforms and

Qualifikationen

• 3+ years of experience in software engineering, systems engineering, or MLOps with focus on AI/ML systems.
• Proficiency in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).
• Experience with cloud platforms (AWS, Azure, GCP) and AI/ML services.
• Strong understanding of containerization (Docker) and orchestration (Kubernetes).
• Familiarity with CI/CD practices and data pipelines/ETL processes.

Aufgaben

• Design, implement, and maintain scalable AI/ML infrastructure and pipelines.
• Optimize ML models for production performance and scalability.
• Develop tools for model training, evaluation, deployment, and monitoring.
• Collaborate with data scientists to productionize research models.
• Automate ML workflows including data preprocessing, feature engineering, and deployment.
• Implement MLOps best practices, CI/CD, and container orchestration.

Kenntnisse

Python

CI/CD

Teamwork

Problem solving

Ausbildung

Bachelor's or Master's degree in CS/Engineering

Tools

Docker

Kubernetes

TensorFlow

PyTorch

Scikit-learn

AWS

Azure

GCP

Jobbeschreibung

Placements24 is seeking an experienced AI/ML Systems Engineer to design, build, optimize, and deploy robust AI/ML systems in Soweto, Gauteng. You will collaborate with data scientists, software engineers, and researchers to ensure high-performance production environments and seamless integration with core products and services.

The role emphasizes building scalable pipelines, automating ML workflows, and applying MLOps best practices, including Docker and Kubernetes, across cloud platforms and