Role Summary:

This is an opportunity to build AI capability from the ground up, working across the full AI stack from infrastructure and data engineering through to application development and business implementation. You'll start by working across multiple areas to understand the AI ecosystem, then progressively specialize in 1-2 areas aligned with your strengths and our strategic needs.

Responsibilities:

• AI Solution Development & Deployment: Design, train, and fine-tune ML/DL models (NLP, computer vision, time series, generative AI). Build and deploy AI applications using LLMs and ML frameworks. Implement prompt engineering, RAG architectures, and agent-based systems. Create APIs and integrations. Deploy models to production via APIs and microservices. Write production-quality Python code.

• Data Engineering & Pipeline Management: Gather, clean, transform, and engineer features from raw data. Design and implement data pipelines using modern ETL/ELT frameworks. Work with SQL, NoSQL, and vector databases. Build and optimize data warehouse architectures. Ensure data quality, governance, and preparation for ML models. Build real-time data processing workflows and implement data observability.

• AI Operations & Infrastructure: Deploy and manage ML models in production. Integrate AI models with existing systems. Implement model evaluation, experimentation frameworks, and A/B testing. Build CI/CD pipelines for AI deployment. Work with containerization (Docker) and orchestration (Kubernetes). Set up model versioning, monitoring, and lifecycle management. Monitor performance, detect drift, and manage retraining cycles. Manage cloud infrastructure for AI workloads. Debug production issues.
  

• Platform Engineering: Build internal AI platform and tooling infrastructure. Implement infrastructure-as-code. Work with microservices architecture and API gateway patterns. Build workflow orchestration for complex AI pipelines. Deploy solutions on cloud platforms. Manage GPU computing resources.
  

• Business Collaboration: Work with product managers, business stakeholders, software engineers, and data scientists. Translate business problems into AI use cases and technical solutions. Identify automation opportunities. Evaluate business value and feasibility of AI use cases. Apply responsible AI and ethical practices. Ensure models are fair, explainable, and respect privacy laws. Implement data governance frameworks.

• Matric / Grade 12
• Bachelor's or Honours degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, or related quantitative field.

Additional Requirements:

• Strong programming skills in Python or similar languages (Java, JavaScript, Go). Solid understanding of data structures, algorithms, and software design principles. Strong SQL skills and database concepts. Familiarity with Git. Understanding of statistics, probability, and data analysis fundamentals. Basic understanding of ML concepts and algorithms. Exposure to cloud platforms (AWS, Azure, or GCP). 
• Understanding of software development lifecycle and best practices. Familiarity with Agile methodologies (Scrum, Kanban). Awareness of DevOps and CI/CD principles. Structured problem-solving approach and analytical thinking.
• Comfort with ambiguity in a rapidly evolving field. Ability to learn new technologies rapidly. Excellent communication skills for both technical and business audiences. Self-starter who can work independently and collaboratively. Willingness to take ownership of technical challenges.

Application Requirements:

1. Detailed CV including academic transcripts and final marks
   2. Cover letter (max 500 words) explaining your interest in AI engineering and which aspects of the AI stack interest you most
   3. Two references (academic or professional)