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.
Desired Skills:
- Machine Learning
- Cloud
- Python
- ML Frameworks
- API Integration
- ML Development
Desired Qualification Level:
- Diploma
About The Employer: