DG
Machine Learning Engineer; Specialist
DLK Group
Cape Town · On-site Contract Senior Yesterday
About the role
About
The role of the Specialist Machine Learning Engineer encompasses many activities, including (but not limited to):
Responsibilities
- Focusing on niche areas of machine learning, such as natural language processing, computer vision, or reinforcement learning.
- Developing domain-specific ML models tailored to specialized business needs.
- Conducting in-depth research and prototyping innovative solutions using advanced ML techniques.
- Identifying opportunities to apply cutting-edge machine learning advancements to improve processes or create new capabilities.
- Collaborating with other Engineers to transfer research findings into scalable, production-ready solutions.
- Providing expert insights on specific tools, frameworks, or algorithms, ensuring the organization stays ahead in ML innovation.
- Contributing to the development of internal ML tools and libraries to streamline workflows.
Requirements
- NQF 6 or higher tertiary qualification in Information Communication Technology (ICT) field incorporating (but not limited to) Information Systems.
- Cloud certification.
- Minimum of 6 years’ experience in a field of a Machine Learning Engineer role.
Requirements
- NQF 6 or higher tertiary qualification in Information Communication Technology (ICT) field incorporating (but not limited to) Information Systems
- Cloud certification
Responsibilities
- Focusing on niche areas of machine learning, such as natural language processing, computer vision, or reinforcement learning.
- Developing domain-specific ML models tailored to specialized business needs.
- Conducting in-depth research and prototyping innovative solutions using advanced ML techniques.
- Identifying opportunities to apply cutting-edge machine learning advancements to improve processes or create new capabilities.
- Collaborating with other Engineers to transfer research findings into scalable, production-ready solutions.
- Providing expert insights on specific tools, frameworks, or algorithms, ensuring the organization stays ahead in ML innovation.
- Contributing to the development of internal ML tools and libraries to streamline workflows.
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