Senior Machine Learning Engineer

Job Description

The Senior Machine Learning Engineer is responsible for designing, developing, and deploying advanced machine learning solutions that drive business impact. This role requires deep technical expertise, strong problem-solving skills, and the ability to lead complex projects from conception through to production. The incumbent will play a key role in shaping the organisation’s ML strategy, mentoring junior engineers, and ensuring scalable, efficient, and ethical use of AI technologies.

Key Responsibilities :

Design and implement advanced machine learning models and systems to solve complex business challenges.

Lead the end-to-end lifecycle of ML projects, including data preparation, model training, validation, deployment, and ongoing monitoring.

Optimise existing ML models and pipelines for scalability, efficiency, and performance in production environments.

Partner with data engineers, software developers, and business stakeholders to integrate ML solutions seamlessly into existing systems and workflows.

Provide mentorship, guidance, and technical leadership to mid-level and junior ML engineers to ensure knowledge sharing and adherence to best practices.

Develop, maintain, and enforce standards for model governance, documentation, versioning, and reproducibility.

Stay abreast of emerging trends, tools, and technologies in machine learning, MLOps, and AI ethics to continually improve solutions and frameworks.

Requirements

• NQF Level 6 or higher tertiary qualification in an ICT-related field (e.g., Computer Science, Information Systems, Data Science, or related discipline).
• Cloud certification (e.g., AWS, Azure, or GCP) preferred.
• Minimum of 5 years’ experience in a Machine Learning Engineer or similar role.
• Proven track record of designing, developing, and deploying ML models in production environments.
• Experience with modern ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Solid understanding of data engineering, model lifecycle management, and MLOps practices.

Requirements

Machine Learning, Cloud, Python, ML Frameworks, API Integration, ML Development