South Africa: Machine Learning Engineer (Model Deployment/MLOps) (CPT Hybrid/Remote) posted by Datafin

April 12, 2026 Jobs in South Africa

Datafin

South Africa

Other IT/Computer

Job Description

Machine Learning Engineer (Model Deployment/MLOps) (CPT Hybrid/Remote)IT – Analyst, Data Management ~ IT – Software Development
Cape Town – Western Cape – South Africa, Remote

ENVIRONMENT:

A fast-paced FinTech company seeks a passionate Machine Learning Engineer (MLOps focus) to power instant lending decisions – no humans in the loop. Its models drive credit risk, portfolio management, and lifecycle decisioning with the biggest challenge being moving models from Data Science into reliable production systems. Theyre looking for you to bridge that gap and ensure that every model built makes it into productionfast, reliable, and cost-efficient. The ideal candidate will require a Postgraduate Degree in a numerate discipline such as Statistics/Mathematics/Software Engineering or a related field with 3+ years experience in ML Engineering, Data Engineering, or Software Engineering with focus on ML deployment. You also need a proven track record of deploying ML models into production (SageMaker, Lambda, Step Functions, or equivalent), strong SQL, PostgreSQL, Node.js, Python, or JavaScript & AWS infrastructure (EC2, ECS/EKS, S3, Lambda, Glue, Step Functions).

DUTIES:

Model Deployment & MLOps –

Take models from Data Scientists (notebooks, prototypes) and productionize them into scalable APIs and pipelines.
Build CI/CD pipelines for ML: automated testing, validation, deployment, rollback.
Implement monitoring for data drift, model drift, and performance decay with automated alerts and retraining triggers.
Maintain reproducible environments for training and inference (Docker, SageMaker, Lambda, Step Functions).

Infrastructure & Pipelines –

Design AWS-native ML infrastructure optimized for cost and scale (ECS/EKS, SageMaker, Lambda, Glue, Step Functions, S3).
Build ETL/ELT pipelines that prepare structured and nested JSON data from PostgreSQL (BI reporting) and other sources.
Ensure models integrate seamlessly into real-time decisioning engines.

Integration & APIs –

Collaborate with Backend Engineers (Node.js/JavaScript) to integrate ML services into production systems.
Build microservices & APIs for inference, feature engineering, and data transformations.
Ensure low-latency, fault-tolerant services for real-time lending decisions.

Collaboration –

Partner closely with Data Scientists to understand models, features, and assumptions.
Work with Software Engineers to ensure production systems can consume models efficiently.
Act as the bridge between research and Engineering, ensuring models dont get stuck in notebooks.

REQUIREMENTS:

Qualifications

Postgraduate Degree in a numerate discipline such as Statistics, Mathematics, Software Engineering, Computer Science, or a related field.
Relevant AWS Certifications (e.g., AWS Certified Machine Learning Specialty, AWS Solutions Architect).
MLOps-related Certifications or professional courses (e.g., Coursera, Udacity, or equivalent).

MUST-HAVEs –

3+ Years experience in ML Engineering, Data Engineering, or Software Engineering with focus on ML deployment.
Proven track record of deploying ML models into production (SageMaker, Lambda, Step Functions, or equivalent).
Experience building CI/CD pipelines for ML.
Strong Backend/Service Development skills (Node.js, Python, or JavaScript).
Deep experience with AWS infrastructure (EC2, ECS/EKS, S3, Lambda, Glue, Step Functions).
Strong SQL + PostgreSQL skills, including working with deeply nested JSON data.

Nice-to-have skills:

Experience in FinTech, Credit, or Risk Modelling.
Understanding of multi-agent AI systems and advanced Feature Engineering (e.g., NLP on bank statements, credit bureau data).
Cost-optimization experience on AWS.

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In South Africa, the IT and computer industry is a significant contributor to the country’s economy, with a diverse range of job opportunities available across various sectors. Typically, these roles involve working with technology to drive business growth, improve efficiency, and enhance customer experience. Generally, careers in this field require a strong understanding of technical concepts and a willingness to adapt to changing technologies.

When it comes to salary expectations, it’s common for IT and computer professionals in South Africa to earn broad ranges that can vary significantly depending on factors such as experience, company size, and industry sector. Typically, entry-level positions may offer salaries between R400 000 and R600 000 per annum, while more senior roles can command higher figures, often ranging from R800 000 to R2 million or more, depending on the specific requirements of the job.

Common skills for IT and computer professionals in South Africa typically include proficiency in programming languages such as Java, Python, or C++, as well as experience with operating systems like Windows, Linux, or macOS. Data analysis and visualisation tools like Tableau, Power BI, or D3.js are also often required, particularly in roles involving data science or business intelligence. Additionally, IT professionals should be familiar with cloud computing platforms like AWS, Azure, or Google Cloud, as well as cybersecurity principles to protect against threats. Other essential skills may include project management, collaboration tools like Slack or Trello, and a solid understanding of database management systems.

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Industries that commonly employ IT and computer professionals in South Africa include the financial services sector, technology industry, manufacturing sector, and public sector organisations. These sectors often require specialists to design, implement, and maintain complex technological systems, ensuring they operate efficiently and securely.

Career development opportunities for IT and computer professionals in South Africa are diverse and varied. Typically, entry-level roles provide a solid foundation for progression into mid-tier positions, where professionals can take on more senior responsibilities and develop their technical expertise. With experience, it’s common to move into leadership roles or transition into related fields like data science, artificial intelligence, or cybersecurity. Many companies also offer opportunities for training, certification, and continuing education to support the ongoing development of their employees’ skills and knowledge.

This information provides general career guidance. Actual salaries and requirements vary by employer.

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