Senior ML Engineer - Build & Scale Production ML

SRG Recruitment; South Africa; On-site; ZAR 1,000,000 - 1,800,000; Full time

Job summary

SRG Recruitment in South Africa is seeking an experienced Machine Learning Engineer to design, build, and optimise ML models and pipelines that deliver real business impact.

You will own the full ML lifecycle, build MLOps infrastructure, and collaborate with AI Engineers to turn business use cases into production-grade solutions, with strong emphasis on observability and scalable systems.

Qualifications

• Deep understanding of ML algorithms, model selection and evaluation.
• Proficient software engineering with production-grade code.
• Hands-on MLOps: pipelines, experiment tracking, versioning, deployment.
• Hands-on cloud experience, AWS preferred.
• Experience building, deploying and operating ML systems at scale.

Responsibilities

• Design, build, and optimise machine learning models and pipelines that deliver real business impact.
• 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 the product suite.

Skills

Deep machine learning

Software engineering

MLOps

AWS cloud

Model evaluation

Statistics & data modelling

Communication

Mentoring

Problem solving

Education

B.Sc. in Computer Science/Statistics/Data Science

Job description

SRG Recruitment in South Africa is seeking an experienced Machine Learning Engineer to design, build, and optimise ML models and pipelines that deliver real business impact.

You will own the full ML lifecycle, build MLOps infrastructure, and collaborate with AI Engineers to turn business use cases into production-grade solutions, with strong emphasis on observability and scalable systems.