Lead ML Engineer – Predictive Analytics & Platform Innovation

Placements24; George; On-site; ZAR 1,400,000 - 2,000,000; Full time

Benefits offered by this job

Medical aid

Retirement fund

Performance incentives

Training and conferences

Job summary

Placements24 invites applications for a Lead Machine Learning Engineer role based in George, Western Cape. You will spearhead predictive analytics, build scalable ML models, and guide a team of engineers and data scientists.

A strong software engineering background and hands-on ML experience are essential for driving production-grade AI solutions. Key responsibilities include end-to-end model development, ML pipeline design for real-time and batch predictions, and collaboration with diverse

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.

Responsibilities

• Lead the end-to-end development lifecycle of ML models, from data ingestion and feature engineering to model training, validation, and deployment.
• Design and implement robust and scalable ML pipelines for real-time and batch predictions.
• Architect and develop ML systems that can handle large volumes of data efficiently.
• Collaborate with stakeholders to translate business needs into ML projects.
• Mentor junior ML engineers and provide technical leadership.
• Champion MLOps best practices, including monitoring, versioning, and CI/CD.
• Evaluate and adopt new technologies to enhance the ML platform.
• Conduct performance testing and optimization of ML models and systems.
• Ensure reliability, security, and ethical considerations of deployed ML solutions.

Skills

Python

Java

Scala

Scikit-learn

TensorFlow

PyTorch

Spark MLlib

MLOps

AWS

Azure

GCP

Hadoop

Spark

Education

CS/Engineering/DS degree

Job description

Placements24 invites applications for a Lead Machine Learning Engineer role based in George, Western Cape. You will spearhead predictive analytics, build scalable ML models, and guide a team of engineers and data scientists.

A strong software engineering background and hands-on ML experience are essential for driving production-grade AI solutions. Key responsibilities include end-to-end model development, ML pipeline design for real-time and batch predictions, and collaboration with diverse