Get a reply from this employer — a resume and cover letter tailored to exactly what they’re hiring for.
Ozow in Cape Town is seeking a Senior Software Engineer (AI Enabled) to lead design and delivery of AI-powered fintech software, collaborating with cross‑functional teams to translate business problems into robust technical solutions.
You will drive AI-enabled replatforming, architectural decisions, data layer ownership including embeddings and vector search, and secure, scalable cloud deployments (AWS or Azure).
Ozow is a leading fintech company that's redefining digital payments in South Africa and beyond. We're dedicated to making payments more accessible, secure, and convenient for both businesses and consumers. As a fast-growing player in the financial technology sector, Ozow fosters a culture of innovation, diversity, and inclusivity. We believe in pushing the boundaries of what's possible and are committed to making a positive impact on the world through our payment solutions.
Ozow is seeking a Senior Software Engineer (AI Enabled) to join our development team. You'll lead the design and delivery of the solutions that power our digital payment platforms, with AI built into both what we deliver and how we deliver it. You'll shape how AI models are integrated as we replatform and modernise our systems, set the standard for disciplined use of AI tools across the software development lifecycle, and help the wider team adopt these ways of working.
This is a senior, hands‑on engineering role, not an agentic AI role. Your focus is building and modernising software with AI, rather than designing autonomous, long-running agent workflows.
Lead the design and delivery of software solutions with cross-functional teams, translating business problems into technical approaches that meet high‑quality standards.
Lead the replatforming of existing systems onto modern, AI-native services, defining the migration approach, sequencing and risk controls so current platforms stay live, stable and secure.
Design and build features that integrate with large language models (LLMs) beyond simple request and response, including context engineering, embeddings and retrieval-augmented generation (RAG).
Own the data layer behind AI features, including embeddings and vector search, alongside relational and NoSQL data stores.
Write clean, efficient and maintainable code that sets the example for the team, using AI coding tools to move faster without lowering the bar on quality, security or testing.
Define and maintain the team's standards and guardrails for using AI safely across the software development lifecycle, from design and coding to testing, review, deployment and documentation.
Ensure AI-generated output is validated, the non‑deterministic nature of AI models is accounted for, and sensitive and payment data is protected when working with AI tools.
Diagnose and resolve the most complex technical issues, including production incidents, and put fixes in place that stop them recurring.
Own the design and architecture of the systems you work on, including how AI components fit into them, making and documenting trade‑offs across scalability, security, performance and cost.
Ensure services handle high‑volume payment traffic, identifying and resolving performance bottlenecks before they affect customers.
Lead code reviews, including AI-assisted code, setting the bar for code quality and sharing knowledge across the team.
Define the testing approach for your area, including unit and integration tests and test‑driven development where it adds value.^
Establish how the team tests and monitor AI features with non‑deterministic outputs, so they behave reliably in production.
Own your services through to production: containerise services, improve CI/CD pipelines and deploy to cloud environments ( AWS or Azure).
Apply and champion DevOps principles, owning the reliability and monitoring of what the team ships.
Work in an Agile environment, contributing to sprint planning, estimation and prioritisation, and helping the team improve how it delivers.
Create and maintain technical documentation, including architecture decisions and the standards for using AI in development.
Mentor and coach engineers, including in effective and disciplined use of AI tools.
Champion AI-enabled ways of working across engineering, sharing what works and helping colleagues make the shift.