Senior ML Engineer at Games Global Operations Limited

Cape Town, Western Cape, South Africa -

Full Time

Start Date

Immediate

Expiry Date

08 Sep, 26

Salary

0.0

Posted On

10 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, MLOps, Software Engineering, AWS, Data Modeling, Statistics, Mathematics, Model Monitoring, Feature Engineering, Pipeline Optimization, Mentoring, Problem Solving

Industry

Gambling Facilities and Casinos

Description

Overview We're looking for a Senior ML Engineer to join our Advanced Data Products team and help build the machine learning systems that power Games Global's data-driven products. If you're obsessed with building robust, scalable ML pipelines — and take pride in models that don't just work in a notebook but thrive in production — this role was made for you. Responsibilities What you'll be doing Design, build, and optimise machine learning models and pipelines that deliver real business impact across GGL Own the full ML lifecycle — from data ingestion and feature engineering through to training, evaluation, deployment, and monitoring Build and maintain MLOps infrastructure to support reliable, repeatable model delivery Collaborate with AI Engineers and data teams to turn business use cases into production-grade ML solutions Build and maintain observability tooling to monitor model performance, drift, and data quality in production Mentor junior engineers in the team — sharing knowledge, reviewing work, and helping them grow Continuously improve model quality, efficiency, and scalability across GGL's product suite Qualifications What we're looking for 🤖 Deep machine learning expertise — strong grasp of algorithms, model selection, and evaluation 🏗️ Solid software engineering skills — you write clean, production-ready code ⚙️ Hands-on MLOps experience — pipelines, experiment tracking, model versioning, and deployment ☁️ Hands-on cloud experience, AWS preferred 🚀 Track record of building, deploying, and operating ML systems at scale 📡 Experience with observability — monitoring, alerting, and keeping models healthy in production 🔢 Strong foundations in statistics, mathematics, and data modelling 🎯 Sharp problem-solving instincts — you break complex technical challenges into structured, deliverable pieces 🤝 Clear communicator who can work effectively across engineering and data teams 👩‍🏫 Experience mentoring, supporting, and growing junior engineers 🎓 B.Sc. in Computer Science, Statistics, Applied Mathematics, Data Science, or related field — B.Eng./B.Tech also considered (postgrad a bonus)

Responsibilities

Design and optimize scalable machine learning models and pipelines across the full ML lifecycle. Build MLOps infrastructure and observability tooling while mentoring junior engineers to ensure production-grade solutions.