Specialist: Machine Learning

Centurion - Responsible to support data science projects and solutions by leveraging service engineering and ML Ops experience to solve a variety of use cases across the Group and for its customers. Expected to be highly skilled in setting up and supporting a MLOps practice and framework with theability to design, build and scale MLOps components and services for new and existing use cases across the group in a cloud environment.RESPONSIBILITIES• Engage with stakeholders to support the design and delivery of data science projects and solutions.• Use service engineering and MLOps techniques to solve business problems.• Responsible for working with a team of MLOps engineers to develop and maintain our cloud-based ML and AI development and production platforms.• Design, build and maintain MLOps repositories, pipelines and components for cloud-based model processing and serving.• Support MLOps framework processes for exploratory data analysis and solution development.• Lead and develop a team of junior MLOps engineers.• Contribute to our agile way of work and our innovation culture.• Up to date knowledge of ML platforms and related technologies.• Translate business requirements into system requirements.• Consistent documentation of all implemented ML models, systems and processes.• Support tools, ML models and infrastructure lifecycles via standard service management principles and processes.• Execute on automation directives by taking repeatable tasks, writing code to replace repeatable tasks, and then adding either a scheduler or some other trigger to enable the job to run automatically while being monitored.• Enable ML and workload orchestration by configuring and controlling systems that can scale horizontally using specialized tools and techniques.• Enable ML and component containerization by isolating individual services into containers, allowing them to run anywhere.• Enabling containers to run and scale horizontally using orchestration tools.• Execute on ML engineering directives by using engineering techniques to automate and scale the ML model life-cycle and host models in a production environmentJob Requirements: REQUIRED CERTIFICATION/PROFESSIONAL REGISTRATION• Data and cloud certifications will be advantageous (GCP, Azure, AWS) as well as certifications for other products in our stack (Kubernetes, Istio, FastAPI,• Docker, PyTorch, Yaml)• QUALIFICATIONS• 3-year degree/ diploma (NQF level 6) preferably in Computer Science, Mathematics, Statistics, Machine Learning or a related field. A relevant post graduate degree will be an added advantageEXPERIENCE• 3-5 years relevant experience, of which at least 2 years must have been in a machine learning operations environment. Experience in ICT/• Telecommunications will be an advantage. Experience with system and process analysis and design.SPECIAL REQUIREMENTS• Experience with Google Cloud Platform.• Expected to stay abreast of new machine learning frameworks and developments and to put them into