Senior AI Engineer, Deep Learning Platform — Remote + Equity

Placements24; Benoni; On-site; ZAR 1,000,000 - 1,500,000; Full time

Benefits offered by this job

Bonuses

Health insurance

Paid time off

Training opportunities

Stock options

Remote work support

Job summary

Placements24 is seeking a Senior AI Engineer with deep learning platform expertise to join our Benoni-based research and development team. You will design scalable DL pipelines, optimize distributed training, and manage ML workloads across cloud or on-prem environments.

You will work closely with AI researchers to productionize models, implement MLOps practices, and contribute to a state-of-the-art AI stack using TensorFlow, PyTorch, or Keras.

Qualifications

• Master's or Ph.D. in Computer Science, Machine Learning, or a related field.
• Minimum of 5 years of experience in software or ML engineering with a focus on deep learning platforms.
• Proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or Keras.

Responsibilities

• Design, implement, and maintain scalable deep learning pipelines and infrastructure.
• Develop and optimize distributed training systems for large-scale neural networks.
• Manage ML workloads on cloud platforms (AWS, Azure, GCP) or on-premises clusters.
• Implement and refine MLOps practices for model deployment, monitoring, and management.
• Collaborate with researchers and data scientists to integrate models into production systems.

Skills

Python

Deep learning

Distributed training

MLOps

TensorFlow

PyTorch

Keras

Education

Master's or Ph.D. in Computer Science, ML, or related field

Tools

Docker

Kubernetes

MLflow

Kubeflow

Spark

CI/CD

Job description

Placements24 is seeking a Senior AI Engineer with deep learning platform expertise to join our Benoni-based research and development team. You will design scalable DL pipelines, optimize distributed training, and manage ML workloads across cloud or on-prem environments.

You will work closely with AI researchers to productionize models, implement MLOps practices, and contribute to a state-of-the-art AI stack using TensorFlow, PyTorch, or Keras.