Senior Specialist: Software Engineer

About the Role

The number 1 Top Employer in South Africa. Certified by the Top Employer Institute 2026.

Responsibilities

• Lead the integration of AI capabilities across the IAM SDLC, identifying and implementing opportunities to improve requirements analysis, software development, testing, deployment, and operational support.
• Drive adoption of AI-enabled engineering tools and practices (e.g., GitHub Copilot, AI-assisted testing, code review, and documentation generation) to improve developer productivity and software quality.
• Provide technical leadership and architecture guidance on the effective, secure, and scalable use of AI within software engineering processes and platforms.
• Establish and enforce AI governance, security, and compliance standards, ensuring responsible use of AI technologies and protection of sensitive data and intellectual property.
• Define and measure engineering performance outcomes, using metrics such as cycle time, deployment frequency, defect rates, and AI adoption indicators to demonstrate value and drive continuous improvement.
• Coach and enable the IAM Enablement team, developing best practices, training, and operating models to embed AI capabilities into day-to-day software development activities.
• Matric essential.
• 3-year Diploma/Degree is essential.
• 5-8+ years' experience in software engineering, systems analysis, solution design, or application development, with a minimum of 3 years operating within Agile delivery environments.
• Demonstrated experience applying Agile methodologies and engineering practices, including Scrum, Kanban, DevOps, CI/CD, test automation, test-driven development (TDD), and modern software delivery frameworks.
• Experience developing and supporting enterprise applications using modern programming languages and frameworks (e.g., Java, Kotlin, SpringBoot, Python, JavaScript/TypeScript, React, SQL, REST/GraphQL APIs, JSON, YAML), with a strong understanding of software design patterns and architecture principles, including microservices; exposure to TM Forum (TMF) patterns is advantageous.
• Experience designing, developing, and operating solutions in cloud-native environments (Azure, AWS, OpenShift, or GCP), including containerisation (Docker/Kubernetes), automation (including infrastructure as code), observability, and platform engineering practices.
• Knowledge and practical application of AI-assisted software engineering practices, including tools such as GitHub Copilot, AI-driven code generation, testing, documentation, or code review capabilities.
• Strong understanding of secure software development, DevSecOps, application security, and responsible AI governance principles.
• Knowledge of Identity and Access Management concepts (access request management, federation, provisioning, authentication, authorisation, access reviews) and protocols (SAML, OAuth 2.0, OpenID Connect, SCIM), and of LDAP and its use in application development.
• Proficiency in Linux and experience with Oracle DB including SQL query optimisation.
• 5-8+ years' experience in software engineering, including solution design, software development, CI/CD, DevOps, and delivery of enterprise-scale applications.
• Demonstrated experience implementing AI-enabled engineering tools and practices, such as GitHub Copilot, AI-assisted testing, code generation, code review, or developer productivity platforms.
• Strong knowledge of modern AI and Generative AI concepts, including Large Language Models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and their practical application within the SDLC.
• Experience establishing secure and governed software development practices, including application security, DevSecOps, data protection, AI governance, and compliance within enterprise environments.
• Proven track record leading technical transformation initiatives, including defining success metrics, driving adoption of new technologies, coaching engineering teams, and delivering measurable improvements in productivity, quality, or delivery outcomes.
• Demonstrate the ability to identify, evaluate and implement technology, automation and AI opportunities that improve team performance, operational efficiency and business outcomes.
• Create an environment that encourages innovation, experimentation, continuous learning and responsible adoption of emerging technologies.
• Lead teams through evolving operating models and technology-enabled transformation, building capability and readiness for future workforce requirements.
• Track and delivers measurable business outcomes from digital, automation and AI initiatives through effective prioritisation, governance and benefits realisation.
• Demonstrate AI fluency and literacy through the ability to effectively use, evaluate and govern AI-enabled tools and solutions, applying them responsibly to improve business outcomes, productivity and decision-making.
• Enticing incentive programs and competitive benefit packages
• Retirement funds, risk benefits, and medical aid benefits
• Cell phone and data benefits, advantages fibre connection discounts, and exclusive staff discounts offered in collaboration with partner companies