Head – Intelligent Engineering & AI Enablement

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Job Summary

We are looking for a transformational technology leader to establish and lead our Intelligent Engineering & AI Enablement capability. This is a new strategic leadership role with a mandate to fundamentally change how the bank builds, tests, deploys and continuously improves technology.

The successful candidate will build a small, high-calibre engineering capability spanning AI and agentic engineering, product engineering, platform engineering, quality engineering, DevSecOps, SRE/AIOps and engineering automation.The mandate is not to replace development partners.

It is to establish a bank-owned engineering capability that uses AI, automation and modern engineering practices to materially improve speed, quality, resilience and engineering economics, while strategically leveraging partners where they add value.

The role reports directly to the Head of Digital Transformation & Technology and works horizontally across Digital Transformation, Technology & Run, Product, Architecture, Cyber & Risk, Group Technology/AI teams and strategic development partners.

Strategic Context

The role is central to the bank’s shift from a traditional Build → Hand-off → Run model towards a Build → Run → Learn → Improve model. Digital Transformation remains accountable for what the bank builds and the business/customer outcomes; Intelligent Engineering & AI Enablement owns how technology is engineered; Technology & Run remains accountable for application and service ownership, production operations, infrastructure, resilience and technology governance.

The successful candidate will therefore be a builder of capability and a catalyst for change - creating the engineering standards, platforms, practices, automation and talent required to make AI-native engineering a practical operating model rather than an isolated technology initiative.

Job Description

Engineering Strategy & Transformation

• Define and execute the Intelligent Engineering & AI Enablement strategy aligned to the bank’s technology and digital transformation priorities.
• Establish an AI-native, product-centric engineering operating model across the software development lifecycle.
• Transform supplier-led delivery towards a bank-owned, AI-augmented engineering model while retaining strategic partner capacity where appropriate.
• Define the right Build / Buy / Partner / Automate / AI approach for engineering capabilities and technology delivery.
• Drive measurable improvements in engineering productivity, speed, quality, resilience and cost.
• Establish engineering performance measures and continuously improve the engineering operating model.

AI & Agentic Engineering

• Lead adoption of AI-assisted software engineering, coding assistants and coding agents across the SDLC.

• Establish practical agentic capabilities across development, testing, documentation, operations and engineering workflows.

• Define reusable AI engineering patterns, components and reference architectures.

• Leverage Absa Group capabilities including the AI Gateway, Databricks, Bedrock and Foundry to accelerate local adoption.

• Establish AI engineering standards, evaluation approaches, guardrails and governance in partnership with Group Technology, Architecture, Cyber, Risk and Responsible AI stakeholders.

• Identify high-value opportunities to use AI to reduce engineering effort, improve quality and accelerate delivery.

Product Engineering

• Build a high-performing AI-native product engineering capability that can rapidly prototype, build and productionise digital solutions.

• Enable modern cloud-native engineering practices, reusable APIs, microservices and components.

• Ensure technical ownership remains within the bank while development partners operate as strategic extensions of the engineering capability.

• Set expectations for engineering quality, maintainability, scalability, security and reuse.

• Partner with Product Owners and Digital Transformation to translate product priorities into effective engineering approaches.

Engineering Platform, DevSecOps & Automation

• Establish modern engineering platforms, developer tooling and reusable engineering capabilities.

• Drive adoption of CI/CD, Infrastructure-as-Code, automated release management and security automation.

• Embed security, quality and compliance controls into engineering pipelines.

• Automate development, testing, deployment, release and environment provisioning processes.

• Use engineering productivity data and AI to continuously improve developer experience and engineering throughput.

Quality Engineering

• Establish continuous quality engineering across the SDLC.

• Drive automated and AI-assisted functional, regression, API, performance and security testing.

• Promote shift-left quality and security practices within engineering teams.

• Use automation and AI-assisted analysis to reduce defect leakage, testing effort and change failure rates.

• Establish quality engineering standards, measures and continuous improvement practices.

Intelligent Operations - SRE & AIOps

• Partner with Technology & Run to establish modern SRE and AIOps capabilities.

• Enable application health observability, reliability engineering and engineering-led operational practices.

• Drive AI-assisted incident analysis, root-cause analysis and operational knowledge management.

• Establish anomaly detection, predictive alerts and automated incident workflows.

• Progressively enable automated remediation and self-healing capabilities where risk and control requirements permit.

• Use production insights and operational data to feed learning back into Product and Engineering.

Engineering Standards, Architecture & Governance

• Establish modern engineering standards, reference architectures, patterns and guardrails.

• Ensure engineering practices align with Absa Group Technology, Architecture, Cyber, Responsible AI and regulatory requirements.

• Promote reuse, standardisation and reduction of technology fragmentation.

• Establish engineering productivity, quality, reliability and automation metrics.

• Provide technical leadership and challenge across engineering decisions without taking ownership of product priorities or production service ownership.

Strategic Development Partner Management

• Transform development partners from primary builders into strategic engineering partners.

• Establish outcome-based engineering partnerships and clear technical accountability.

• Determine which capabilities should be built internally, AI-assisted, automated, bought or sourced through partners.

• Measure partner productivity, quality, cost, engineering maturity and value delivered.

• Continuously optimise the balance between internal engineering capability, AI-enabled development and external capacity.

Leadership & Capability Building

• Build and lead a small, high-performing Intelligent Engineering & AI Enablement team.

• Create an AI-native engineering culture across Digital Transformation & Technology.

• Attract, develop and retain modern engineering and AI talent.

• Coach Product, Application and Service Owners on modern engineering practices and the new operating model.

• Establish an engineering community of practice and promote knowledge sharing and reuse across the bank.

• Build internal capability that can mature and scale with the bank’s strategic needs.

Key Accountabilities

• Intelligent Engineering strategy and transformation

• AI-native engineering adoption and AI-enabled SDLC

• AI and agentic engineering

• Internal engineering capability and engineering culture

• Engineering productivity and automation

• Product and platform engineering practices

• Quality engineering and automated testing

• DevSecOps and engineering security automation

• SRE / AIOps and intelligent operational engineering

• Engineering standards, patterns and governance

• Strategic development partner / supplier engineering model

• Engineering economics and measurable value realisation

What We Are Looking For

This is not a traditional Application Development, IT Operations or Technology Delivery leadership role. We are looking for a technology leader who has personally lived through modern engineering transformation and can credibly challenge established technology delivery models.

A Builder

• Proven experience building, scaling or transforming engineering organisations or capabilities.

• Strong software engineering foundation and technical credibility.

• Able to engage deeply with engineers, architects and technology partners.

An AI-Native Engineering Leader

• Practical experience applying GenAI to software engineering and technology delivery.

• Experience with AI coding assistants, coding agents and/or agentic engineering.

• Understanding of how AI can be embedded across development, testing, documentation and operations.

• Ability to distinguish practical AI-enabled engineering value from experimentation or technology hype.

A Modern Technology Leader

• Strong cloud and platform engineering experience.

• Strong understanding of DevSecOps, CI/CD and Infrastructure-as-Code.

• Experience with automated quality engineering and modern testing practices.

• Understanding of SRE, observability, AIOps and automated operations.

A Transformation Leader

• Proven experience changing technology operating models.

• Experience reducing dependency on traditional supplier-led or project-based delivery.

• Demonstrated improvements in engineering speed, quality, productivity, resilience or cost.

• Comfortable challenging established ways of working and influencing senior stakeholders.

A Strategic Partner

• Strong development partner and vendor management experience.

• Able to determine what should be built internally, AI-assisted, automated, bought or partnered.

• Strong commercial understanding of technology delivery economics.

A Banking / Regulated Technology Leader

• Experience in banking, financial services, fintech or another highly regulated environment preferred.

• Strong understanding of technology risk, cybersecurity, resilience, data protection and regulatory requirements.

• Ability to balance engineering velocity with security, resilience and control.

Preferred Education

• Bachelor’s degree in Computer Science, Engineering, Information Technology or a related discipline.

• Master’s degree in Technology, Computer Science, Engineering, Business Administration or a related field preferred.

• Relevant professional certifications in Cloud, AI/ML, DevSecOps, SRE or Software Engineering are advantageous.

• Strong relevant practical experience may be considered in lieu of formal qualifications.

Preferred Experience

• 12-15+ years of experience across software engineering, technology, digital or platform engineering.

• Proven leadership of modern engineering transformation within a large, complex or regulated organisation.

• Demonstrated experience implementing AI-assisted software development and/or AI-native engineering practices.

• Practical experience with GenAI, LLM applications, agentic AI, RAG and AI engineering platforms.

• Strong experience with cloud-native applications, APIs, platforms and modern engineering practices.

• Strong experience across DevSecOps, CI/CD, automated testing, observability and SRE/AIOps.

• Experience building or scaling internal engineering capability and changing supplier-led delivery models.

• Experience establishing engineering standards, developer productivity practices and modern SDLC models.

• Experience managing strategic technology partners while retaining internal technical accountability.

• Experience in banking, financial services, fintech or another highly regulated environment preferred.

• Experience working with AI governance, cybersecurity, technology risk and regulatory stakeholders.

• Demonstrated ability to influence senior business, technology and executive stakeholders.

• Particularly valuable: experience taking an organisation from traditional/project-based engineering to product-centric, AI-enabled and continuously automated engineering.

Knowledge & Skills

Engineering & Architecture

• Modern software engineering and software architecture

• Cloud-native architecture and platform engineering

• API, microservices and reusable component design

• Engineering standards, patterns and technical quality

AI & Agentic Engineering

• Generative AI and LLM application development

• Agentic AI architecture and AI coding agents

• RAG patterns and AI engineering platforms

• AI evaluation, governance and responsible AI practices

• AI-enabled SDLC and AI-assisted engineering

Engineering Productivity & Automation

• DevSecOps, CI/CD and Infrastructure-as-Code

• Automated testing and release management

• Developer productivity tooling and metrics

• Code quality, security scanning and environment automation

Intelligent Operations

• SRE principles and observability

• AIOps and intelligent incident management

• Anomaly detection and predictive monitoring

• RCA automation and automated remediation / self-healing

Leadership & Transformation

• Technology strategy and engineering operating models

• Capability building and organisational change

• Build / Buy / Partner / Automate decision-making

• Strategic supplier management and commercial acumen

• Executive stakeholder influence

Banking & Risk

• Digital banking and financial services technology

• Technology risk, cybersecurity and resilience

• Data privacy, regulatory controls and governance

• Balancing engineering velocity with control requirements

Leadership Profile

We are looking for a leader who combines software engineering credibility, cloud and platform expertise, AI/GenAI/agentic engineering, DevSecOps and SRE knowledge, transformation leadership and strategic partner management.

The successful candidate must be comfortable operating across Product & Digital Transformation, Technology & Run, Group AI, Architecture, Cyber & Risk, development partners and Executive Leadership. They should have the credibility to sit with engineers and architects, the strategic perspective to engage ExCo, and the influence to bring multiple stakeholders together around a new engineering model.

What Success Looks Like

• Establish the Intelligent Engineering & AI Enablement operating model and roadmap.

• Build the initial high-performing engineering team and capability.

• Establish AI-enabled SDLC practices, engineering standards and measurable productivity metrics.

• Deliver production solutions using AI-native and AI-assisted engineering practices.

• Demonstrate measurable improvements in engineering speed, quality, resilience and cost.

• Reduce unnecessary dependency on external engineering capacity while increasing partner productivity and accountability.

• Establish automated testing, DevSecOps and modern engineering platform capabilities.

• Launch initial AI engineering, developer productivity and AIOps/operational automation use cases.

• Create reusable engineering and AI capabilities that can be adopted more widely across the bank.

• Establish a continuous improvement loop from production insights back into Product and Engineering.

The Opportunity

This is an opportunity to build a new strategic capability at a time when AI is fundamentally changing how software is engineered and technology is operated.

You will not simply inherit an engineering organisation. You will have the opportunity to define how Absa Mauritius engineers technology in the AI era - combining exceptional people, AI, automation, modern platforms, strong engineering practices and strategic partners to create a faster, safer, more resilient and more efficient technology organisation.

Education