Requirements are:

Minimum Qualifications

• An appropriate, recognised qualification at NQF Level 7, with preference for a qualification in Information Technology, Computer Science, Computer Systems or equivalent.
• Solutions Architecture certification, Scaled Agile Framework, Azure, Google, Oracle or AWS cloud architecture certification.
• A relevant NQF level 8 qualification will be an advantage.

Minimum Experience

• Eight years’ relevant work experience.
• Strong expertise in AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.) will be an advantage.
• Understanding data architecture, APIs, and microservices will be an advantage.
• Knowledge of DevOps/MLOps practices for AI model deployment will be an advantage.

Knowledge, skills and competencies

• AI and Machine Learning
• Architecture Design
• Cloud architecture
• Cloud Computing
• Code Quality Management
• Cybersecurity architecture for applications
• Data Management and Design
• Database and data warehouse design
• DevOps and CI/CD
• Governance and compliance
• Javascript programing
• Net programing
• Programming and Scripting
• Python programing
• Software Development Lifecycle (SDLC)
• SQL scripting
• Stakeholder communication
• Technical problem solving
• Tool proficiency
• Resilience, attention to detail and sound judgement.

Key performance areas:

• Design and Implement AI driven Architectures.
• Develop AI Road maps aligned with organisational strategy.
• Assess and recommend AI technologies, tools and platforms.
• Build and deploy AI/ML models ensuring scalability and performance.
• Integrate AI solutions with new, existing enterprise applications and data pipelines.
• Work with data specialists and developers to optimize data processing and model training workflows.
• Implement change requests on existing applications.
• Oversee model deployment using cloud services and on-premises environments.
• Adhere to clean code principles and maintainable design patterns.
• Resolve identified risks and issues.
• Work closely with software engineers, data stewards and Business Analysts.
• Provide technical guidance and mentorship to the development AI teams.
• Collaborate with Stakeholders to define AI-driven business use cases.
• Advocate for best practices in AI ethics, security and compliance.
• Stay updated on emerging AI technologies.
• Conduct feasibility studies on new AI applications and enhancements.
• Drive the adoption of responsible and ethical AI practices