Machine Learning Engineer
Machine Learning Specialists are in demand to create and deploy predictive models, improve algorithms, and optimise performance. Key responsibilities include data preprocessing, model training, evaluation, and deployment. Applicants should have a solid grounding in mathematics, Python, and ML libraries such as scikit-learn, XGBoost, or Keras.
Job Overview:
We are looking for a skilled Machine Learning Engineer to develop, deploy, and maintain predictive models and intelligent systems for data-driven organisations. You will work alongside data scientists and software engineers to implement machine learning solutions that solve complex business challenges. This role is ideal for individuals with strong coding skills, a deep understanding of data science principles, and a keen interest in model optimisation.
Employment Type:
Full-time or freelance contract (depending on role availability)
Remuneration:
Market-related, commensurate with experience and project scope
Reporting Line:
Reports to Lead Data Scientist, AI Engineering Manager, or Head of Analytics
Working Conditions:
Location: South Africa or remote
Work Environment: Flexible, collaborative teams working across time zones
Equipment and tools: Cloud infrastructure access, development tools, and performance monitoring platforms
Key Responsibilities:
Design and develop machine learning models for classification, regression, clustering, and recommendation systems.
Translate business problems into data science solutions by selecting appropriate algorithms and modelling approaches.
Preprocess, clean, and transform data to prepare for training and testing.
Train, evaluate, and fine-tune models using metrics such as precision, recall, AUC, and F1 score.
Deploy machine learning models into production environments using CI/CD pipelines and cloud infrastructure.
Collaborate with product and engineering teams to integrate models into applications and workflows.
Monitor model performance in production and implement updates based on data drift or performance degradation.
Document model design, performance, and usage guidelines.
Required Skills and Competencies:
Proficiency in Python and libraries such as scikit-learn, XGBoost, pandas, NumPy, and matplotlib.
Strong understanding of machine learning concepts, including supervised and unsupervised learning, model evaluation, and optimisation.
Experience with cloud-based environments such as AWS SageMaker, Google Vertex AI, or Azure Machine Learning.
Familiarity with data engineering and model deployment tools (e.g. Docker, MLflow, Airflow).
Ability to write clean, maintainable code and work in collaborative development environments (Git, GitHub/GitLab).
Excellent analytical and problem-solving skills.
Preferred Qualifications and Experience:
Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field.
2–5 years of hands-on experience building and deploying machine learning models.
Exposure to deep learning frameworks (e.g. TensorFlow, PyTorch) is a plus.
Experience working with large datasets or streaming data platforms (e.g. Apache Spark, Kafka) is advantageous.