Cape Town: Intermediate Machine Learning Engineer posted by Sabenza IT & Recruitment

The Intermediate Machine Learning Engineer is responsible for developing, optimizing, and deploying machine learning solutions that support data-driven decision-making and business objectives. The role requires strong technical expertise in model development, pipeline management, and integration within production environments.

Key Responsibilities:

• The role encompasses many activities, including (but not limited to):
  
• Building and maintaining end-to-end machine learning pipelines for model development, training, testing, and deployment.
  
• Training and fine-tuning ML models using structured and unstructured datasets.
  
• Collaborating with Senior Engineers and Data Scientists to implement ML models into production environments.
  
• Conducting model evaluation and validation to ensure accuracy, scalability, and alignment with business goals.
  
• Troubleshooting and resolving issues related to model performance, accuracy, and deployment.
  
• Documenting workflows, maintaining version control, and ensuring reproducibility of ML experiments.
  
• Supporting the integration of ML models with existing software systems and data infrastructures.
  
• Keeping up-to-date with emerging tools, frameworks, and trends in machine learning and AI.
  

Requirements

• NQF Level 6 or higher tertiary qualification in an ICT-related field, such as Information Systems, Computer Science, Data Science, Software Engineering.
  
• Preferred Certifications: Cloud platform certification (AWS, Azure, or GCP) with specialization in ML or AI services.
  
• Minimum of 3 years experience in a Machine Learning Engineer role or a similar position.
  
• Proven experience developing, deploying, and monitoring machine learning models in production.
  
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  
• Experience with cloud-based ML services and tools (AWS SageMaker, Azure ML, GCP Vertex AI).
  
• Familiarity with containerization (Docker, Kubernetes) and CI/CD practices for ML Ops
  
• Strong programming skills in Python (and optionally R or Java).
  
• Proficiency in data preprocessing, feature engineering, and model evaluation techniques.
  
• Experience working with APIs and integrating ML models into production systems.
  
• Solid understanding of software engineering principles and version control (Git).
  
• Strong analytical, problem-solving, and debugging skills.
  
• Excellent collaboration and communication abilities within cross-functional teams.