Standard Bank Group is a leading Africa-focused financial services group, and an innovative player on the global stage, that offers a variety of career-enhancing opportunities – plus the chance to work alongside some of the sector’s most talented, motivated professionals. Our clients range from individuals, to businesses of all sizes, high net worth families and large multinational corporates and institutions. We’re passionate about creating growth in Africa. Bringing true, meaningful value to our clients and the communities we serve and creating a real sense of purpose for you.

• Lead the technical execution and engineering delivery of AI and GenAI solutions across Group Risk, ensuring scalable, secure, and production-ready implementations.
• Translate business problems and strategic objectives into clear technical requirements, solution architectures, and measurable AI use cases, ensuring alignment between stakeholder needs and engineering delivery.
• Partner closely with business stakeholders, risk teams, and product owners to shape and prioritise high-value AI opportunities, conducting rapid prototyping and proof-of-value exercises to assess feasibility and impact.
• Design solution architectures and technical patterns for AI use cases, producing high-quality solution designs, technical documentation, and architecture artefacts.
• Drive the implementation and optimisation of Virtual Risk Manager / AI assistant capabilities, including LLM adoption, orchestration, retrieval, and performance improvements.
• Build and automate ML/LLM pipelines, enabling rapid experimentation, evaluation, monitoring, and deployment through robust engineering practices.
• Present solution designs and technical approaches at architecture forums, governance committees, and senior stakeholder engagements.
• Research and apply emerging AI techniques and technologies to improve efficiency, insight generation, automation, and decision-making across Group Risk.

Minimum Qualifications

•  Post Graduate Degree Information Technology

Technical Skills & Experience

• Strong hands-on experience designing, building, and deploying AI/ML and GenAI solutions on Microsoft Azure, including Azure OpenAI, Azure AI Foundry, Azure AI Services, Azure Machine Learning, Azure Kubernetes Service (AKS), Azure Container Apps, APIs, and cloud-native architectures.
• Deep understanding of machine learning, large language models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, model evaluation, fine-tuning approaches, agentic AI systems, multi-agent orchestration, and conversational AI architectures.
• Strong software engineering discipline, including Python development, API development, source control (Git), CI/CD pipelines, automated testing, containerisation, DevOps practices, reusable code patterns, and secure production-grade engineering standards.
• Experience with major AI/ML frameworks and tooling such as PyTorch, TensorFlow, scikit-learn, LangChain, LlamaIndex, Semantic Kernel, vector databases, model orchestration frameworks, and observability/evaluation tooling for AI systems.
• Experience building end-to-end AI products and intelligent applications, including integration of AI models into enterprise systems through APIs, batch, streaming, and event-driven architectures, ensuring scalability, reliability, and maintainability.
• Strong experience working with structured and unstructured data, including feature engineering, embeddings, knowledge retrieval, document processing, semantic search, experimentation, and rapid prototyping.
• Experience developing business-facing AI applications and interfaces using Python frameworks and modern web technologies to enable intuitive interaction with AI capabilities.
• Familiarity with data visualisation and insight tools (e.g., Power BI) to support business consumption, explainability, and interpretation of AI-driven outputs.
• Experience implementing MLOps and LLMOps practices, including model lifecycle management, experimentation, monitoring, prompt/version management, evaluation, observability, and production support.
• Understanding of responsible AI, model governance, explainability, bias monitoring, security, and risk controls required for enterprise AI deployments in regulated environments.

Preferred Experience

• Exposure to AI governance, model risk management, responsible AI, monitoring, explainability, and production model lifecycle management (MLOps/LLMOps).
• Experience leading or mentoring engineers and data scientists while driving execution in a fast-paced delivery environment.
• Proven ability to engage and influence senior stakeholders, including executive leadership (e.g., CROs, Risk Executives, CIOs, senior governance forums), translating complex technical concepts into clear business language and influencing decision-making.
• Strong executive communication and stakeholder management capability, with experience presenting at senior committees, architecture forums, governance bodies, and business leadership engagements.
• Experience leading or mentoring engineers and data scientists, while providing technical leadership and execution oversight across complex AI programmes.

Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Checking Things
• Examining Information
• Exploring Possibilities
• Interacting with People
• Interpreting Data
• Meeting Timescales
• Producing Output
• Providing Insights
• Team Working

Technical Competencies:

• Data Analysis
• Database Administration
• Data Integrity
• Knowledge Classification
• Research & Information Gathering