QUALIFICATIONS and EXPERIENCE Relevant Degree or Diploma in Computer Science, Data Science, Artificial Intelligence, Information Systems, Engineering, or a related field. Certifications such as Microsoft Certified: Azure AI Engineer Associate, Azure Data Scientist Associate, or equivalent (preferred but not mandatory). Minimum 3+ years of experience in AI/ML engineering, data science, or building AI-enabled solutions in a production environment. Hands-on experience developing and deploying LLM and/or generative AI solutions (e.g. RAG, chatbots, AI agents, intelligent document processing). Experience integrating AI services into existing business applications, ideally within the Microsoft ecosystem (Azure, Power Platform, Microsoft 365). Proven track record of delivering AI solutions that achieved measurable business outcomes. Experience working in cross-functional teams alongside business stakeholders, developers, and data engineers. KEY SKILLS Strong proficiency in Python and common AI/ML libraries (e.g. scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel). Hands-on experience with Azure AI Services, Azure OpenAI, Azure Machine Learning, and Azure AI Foundry. Experience building LLM-based solutions: prompt engineering, RAG, embeddings, vector databases, and AI agents. Working knowledge of Microsoft Power Platform (Power Apps, Power Automate, Power BI, Dataverse) and AI Builder / Copilot Studio. Solid understanding of data engineering: SQL, APIs, data pipelines, and working with structured and unstructured data. Familiarity with MLOps, version control (Git), CI/CD, and cloud deployment patterns. Strong analytical, problem-solving, and solution-design abilities. Excellent interpersonal and communication skills; able to translate business needs into technical solutions and vice versa. Awareness of Responsible AI principles, data privacy, and AI security best practices. DUTIES and RESPONSIBILITIES AI Solution Design & DevelopmentIdentify, evaluate, and prioritise AI/ML use cases across the business and translate them into deployable solutions. Design, develop, and deploy AI models and intelligent applications using Python, Azure AI Services, Azure OpenAI, and related frameworks. Build and fine-tune Large Language Model (LLM) solutions, including Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, and AI agents. Develop predictive models, classification systems, computer vision pipelines, and intelligent document processing solutions where applicable. Manage the full AI solution lifecycle: data exploration, model development, testing, deployment, monitoring, and continuous improvement. Integration With Existing SolutionsEmbed AI capabilities into existing Microsoft Power Platform solutions (Power Apps, Power Automate, Power BI, Power Pages, Dataverse) using AI Builder, Azure AI Foundry, and custom connectors. Integrate AI services into current line-of-business systems through APIs, custom connectors, Azure Functions, and Logic Apps. Enhance Power BI reporting with AI-driven insights, forecasting, anomaly detection, and natural-language Q&A. Build Copilot Studio agents and conversational interfaces that interact with internal systems and data sources. Data Engineering & MLOpsWork with structured and unstructured data sources to prepare, clean, and engineer features for AI models. Implement MLOps practices including version control, CI/CD for models, monitoring, drift detection, and responsible retraining. Ensure AI solutions are secure, scalable, observable, and aligned with cloud architecture best practices on Microsoft Azure. Cross-functional Collaboration & AdoptionPartner with departments to understand workflows and identify where AI can deliver measurable impact. Collaborate with the Digital Transformation team to align AI initiatives with the broader digital roadmap. Provide user training, documentation, and ongoing support to drive adoption of AI-enabled tools. Communicate complex AI concepts in clear, business-friendly language to non-technical stakeholders. AI Governance, Ethics & RiskApply Responsible AI principles: fairness, transparency, accountability, privacy, and security. Establish and enforce governance frameworks for AI usage, including data handling, model approval, and acceptable-use policies. Monitor model performance, bias, and hallucination risks; implement guardrails for generative AI solutions. Ensure compliance with POPIA, Company Group standards, and applicable data protection and security requirements. Innovation & Continuous ImprovementStay current with advances in AI, generative models, agentic systems, and the Microsoft AI stack. Lead AI proofs-of-concept and pilots, then scale successful initiatives enterprise-wide. Review existing systems and recommend AI-driven enhancements that improve performance, accuracy, or user experience. Project & Documentation ManagementManage multiple AI initiatives concurrently from conception through deployment and handover. Produce technical and functional documentation including solution architecture, data flows, model cards, SOPs, and support guides. Ensure adherence to company standards, security requirements, and statutory regulations.