Machine Learning Engineer

Cape Town - South Africa

Monthly Salary: Not Disclosed

Experience Required: 4-5years

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

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.

Required Skills:

Machine Learning Cloud Python ML Frameworks API Integration ML Development

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.