Machine Learning Engineer

E-Merge IT Recruitment; Cape Town; Hybrid; ZAR 1 190 000 - 1 400 000; Full time

Job summary

A recruitment firm is seeking a Machine Learning Engineer in Cape Town to drive the bank's AI strategy. This role involves designing scalable machine learning models and deploying AI solutions. Candidates should have a Master’s degree, strong Python skills, and experience with big data frameworks like Hadoop and Kafka. This hybrid position offers a salary of up to R1.4mil CTC, negotiable based on experience.

Qualifications

• Master's degree in information technology, computer science, engineering, or a related field.
• Strong Python programming skills and experience with ML libraries.
• Solid SQL knowledge for data extraction and optimization.
• Experience with big data frameworks like Hadoop and Kafka.
• Proven experience deploying and maintaining ML models.

Responsibilities

• Design, build, and deploy scalable machine learning models for credit applications.
• Partner with data scientists to enhance model performance.
• Develop and maintain machine learning pipelines and platforms.
• Apply big data frameworks for large-scale data processing.
• Ensure models meet rigorous production standards.

Skills

Python programming

Machine Learning libraries (scikit-learn, TensorFlow, PyTorch)

SQL knowledge

Big data frameworks (Hadoop, Kafka, PySpark)

Version control (Git / GitHub)

AWS project experience

Education

Master's degree in IT, Computer Science, Engineering, or related field

Tools

AWS

Hadoop

Kafka

PySpark

Job description

Join a Credit Division as a Machine Learning Engineer, where you’ll help bring the bank’s AI strategy to life. Working alongside Data Scientists and Decision Scientists, you’ll design, implement, and enhance machine learning platforms while driving the delivery of scalable AI solutions. The effective use of AI will be a defining competitive advantage, and this role offers the chance to shape that future. You’ll take models from concept through to production—applying modern engineering practices, big data processing, and cloud technologies—to build predictive solutions that have a direct impact on our clients and business.

Responsibilities

• Design, build, and deploy scalable machine learning models for real-world credit applications.
• Partner with data scientists and decision scientists to enhance model accuracy, efficiency, and performance.
• Develop, maintain, and improve machine learning pipelines and platforms.
• Apply big data frameworks (Hadoop, Kafka, PySpark) for large-scale data processing.
• Ensure models meet rigorous production standards for scalability, robustness, and maintainability.
• Leverage cloud platforms (AWS preferred) for deploying and managing AI solutions.
• Follow software engineering best practices, including version control (Git / GitHub) and peer reviews.
• Stay ahead of emerging trends in AI, ML, and big data to recommend innovative solutions.

Qualifications and Experience

Master’s degree in information technology, computer science, engineering, or related field. Strong Python programming skills with experience in ML libraries (scikit-learn, TensorFlow, PyTorch). Solid SQL knowledge for data extraction, transformation, and optimisation. Experience with PySpark and distributed data processing. Hands‑on exposure to big data frameworks (Hadoop, Kafka). Proven experience deploying and maintaining ML models in production environments. Proficiency in version control (Git / GitHub). AWS certification or strong AWS project experience (highly advantageous). Credit scoring: Logistic Regression, XGBoost, LightGBM. Default prediction: Random Forest, GBMs, Logistic Regression. Loan approval automation: Decision Trees, GBMs. Fraud detection: Neural Networks, Isolation Forest, SVM. Collections prioritisation: Clustering, Gradient Boosting. Customer segmentation: K‑means, DBSCAN, Hierarchical Clustering.

The reference number for this position is NG60786 which is a permanent, hybrid position in Cape Town offering a salary of up to R1.4mil CTC salary negotiable based on experience.