Deep Learning Pipelines Engineer — ML & MLOps

Placements24; Stellenbosch; Hybrid; ZAR 700,000 - 1,100,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Paid time off

Training opportunities

Modern office

Job summary

Placements24 in Stellenbosch, Western Cape, is seeking a skilled Machine Learning Engineer to design and optimize robust deep learning pipelines powering state-of-the-art AI applications. You will own the end-to-end lifecycle of ML models from data prep to deployment, collaborating with researchers and software engineers.

The ideal candidate has 2–4 years in ML engineering, strong Python, experience with TensorFlow/PyTorch/JAX, and hands-on MLOps and cloud services.

Qualifications

• Bachelor's or Master's in CS/Engineering.
• 2-4 years of ML engineering experience.
• Proficiency in Python and DL frameworks.
• Experience with cloud ML services (AWS/Azure/GCP).
• Familiarity with MLOps tools and CI/CD.
• Experience with data processing libraries (pandas, Dask) and SQL.

Responsibilities

• Design, build, and maintain scalable deep learning training and inference pipelines.
• Train, evaluate, and fine-tune deep learning models using TensorFlow, PyTorch, or JAX.
• Deploy ML models into production with CI/CD pipelines.
• Monitor model performance and handle drift/retraining strategies.
• Collaborate with researchers and data scientists to iterate on models.
• Write clean, efficient, and well-documented code.

Skills

Python programming

Problem solving

Teamwork

Debugging

Education

Bachelor's degree in CS

Master's degree in CS/Engineering

Tools

TensorFlow

PyTorch

JAX

Docker

Kubernetes

MLflow

Job description

Placements24 in Stellenbosch, Western Cape, is seeking a skilled Machine Learning Engineer to design and optimize robust deep learning pipelines powering state-of-the-art AI applications. You will own the end-to-end lifecycle of ML models from data prep to deployment, collaborating with researchers and software engineers.

The ideal candidate has 2–4 years in ML engineering, strong Python, experience with TensorFlow/PyTorch/JAX, and hands-on MLOps and cloud services.