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