Lead Engineer: AI at Nedbank

Job Purpose

• To design, build, deploy, and support practical AI-enabled automation solutions that improve the efficiency, effectiveness, and control of forensic and fraud operations.
• The role is focused on hands-on implementation of tools and workflows that solve real business problems, primarily within Nedbank’s current Microsoft and Azure technology environment, while contributing to the evolution of future-fit solution patterns over time.
• The role requires sound engineering discipline, appropriate governance, human oversight, traceability, and a strong focus on measurable business value.

Job Responsibilities

• Design, develop, test, deploy, and maintain AI-enabled solutions that support fraud and forensic operations.
• Translate operational problems into practical technical solutions using software engineering, automation, and AI tools where appropriate.
• Build and support solutions such as document extraction, workflow automation, case support tools, search and retrieval tools, summarisation, classification, and human-in-the-loop decision support.
• Design and implement solutions primarily within Nedbank’s current Microsoft and Azure-based platforms, services, and integration patterns.
• Integrate AI-enabled solutions with internal systems, databases, APIs, case management tools, workflow platforms, and reporting environments.
• Implement controls within solutions, including validation, exception handling, approval steps, audit trails, logging, monitoring, and escalation paths.
• Work with structured and unstructured data, including text, documents, images, audio, and other operational data sources where relevant.
• Ensure solutions are supportable and production-ready through sound engineering practice, including testing, version control, documentation, performance monitoring, and operational support readiness.
• Work closely with fraud, forensic, operations, technology, data, and risk stakeholders to identify use cases, prioritise opportunities, and deliver measurable business value.

Job Responsibilities Continued

• Evaluate tools and approaches pragmatically, with emphasis on usefulness, cost-effectiveness, maintainability, governance, and fit for Nedbank’s environment.
• Support compliance with internal standards relating to data protection, information security, governance, responsible use of AI, and operational risk.
• Contribute to continuous improvement within the team by identifying opportunities to simplify processes, reduce manual effort, improve control, and improve quality of output.
• Participate in knowledge-sharing and contribute to a culture conducive to transformation, collaboration, and continuous learning.
• Support Nedbank’s business strategy by delivering work in a way that reflects the bank’s values, governance standards, and client-centric approach.
• Participate in corporate social investment and other organisational initiatives where required.

Essential Qualification

• Matric / Grade 12 / National Senior Certificate.
• Relevant practical experience in software engineering, automation, AI implementation, or a related technical field.
• A relevant diploma or degree in Computer Science, Information Systems, Engineering, Data Science, or a similar field would be advantageous, but is not essential where equivalent practical ability can be demonstrated.
• Relevant certifications in software development, cloud platforms, automation, or AI would be advantageous.

Essential Experience

• Demonstrable experience building working technical solutions rather than only producing concepts, research, or analysis.
• Hands-on software development experience in Python essential, C# highly advantageous.
• Experience integrating systems through APIs, working with databases, and automating business processes.
• Experience taking solutions from prototype stage into reliable operational use.
• Experience working with structured and unstructured data.
• Experience implementing controls such as logging, exception handling, validation, approval steps, and monitoring.
• Experience working with business stakeholders to understand requirements and translate them into usable technical solutions.
• Experience working in an enterprise technology environment.

Preferred / Advantageous Experience

• Experience working within Microsoft and Azure enterprise environments is strongly preferred.
• Exposure to technologies such as Azure AI Services, Azure OpenAI, Power Platform, AI Builder, Copilot Studio, Microsoft Fabric, or related Microsoft tools would be advantageous.
• Experience with AI-enabled solutions involving documents, images, audio, or other unstructured data sources would be advantageous.
• Experience implementing controlled workflow automation using LLMs, retrieval, tool orchestration, or human-in-the-loop decisioning would be advantageous.
• Experience in banking, fraud, forensics, compliance, risk, or another regulated environment would be advantageous.
• Experience building tools that support operational teams rather than purely analytical or research use cases would be advantageous.
• Experience working within governance, information security, or data privacy requirements in an enterprise environment would be advantageous.

Technical Competencies

• Software development and problem solving.
• Python or similar programming capability.
• API and systems integration.
• Database querying and data handling.
• Workflow automation and orchestration.
• Application of AI tools and services to business problems.
• Ability to implement AI and automation solutions within Microsoft and Azure-based enterprise environments.
• Understanding of cloud-based AI services, workflow automation, integration patterns, identity and access considerations, and support requirements within the Microsoft stack.
• Prompt design, retrieval-based solutions, and controlled use of large language models.
• Ability to work with structured and unstructured data, including text, documents, images, and audio.
• Understanding of controlled AI workflow patterns, including retrieval, orchestration, validation, approval steps, and human oversight.
• Testing, deployment, monitoring, and operational support.
• Understanding of traceability, auditability, and control requirements in enterprise solutions.
• Ability to evaluate and apply fit-for-purpose tools and solution patterns pragmatically, taking into account business value, cost, supportability, governance, and long-term maintainability.

Behavioral Competencies

• Applied Learning
• Building Customer Loyalty
• Communication
• Decision Making
• Earning Trust
• Initiating Action
• Innovation
• Managing Work
• Quality Orientation
• Stress Tolerance
• Work Standards

Key Performance Expectations / Success Measures

• Delivery of practical AI and automation solutions that improve productivity, quality, control, or turnaround time in fraud and forensic operations.
• Reduction of manual effort through controlled and supportable automation.
• Development of maintainable and reliable solutions with appropriate documentation, monitoring, and auditability.
• Demonstrable business value from implemented use cases.
• Constructive collaboration with business and technical stakeholders.
• Adherence to governance, risk, compliance, and responsible AI requirements.

Closing date: 28 April 2026