Full Stack Engineer (Johannesburg)

A high-growth AI and fintech organisation is seeking a Full Stack Engineer with a strong Python background to join its innovative, intelligent team. This is an opportunity get involved in cutting-edge AI technology that is changing the way wealth-management professionals access information, generate insights and engage with their clients.
You will be a key member of the engineering team, contributing across the full development lifecycle and helping turn ambitious product ideas into scalable, production-ready solutions.
If you are a Python-focused Full Stack Engineer who wants to work on ambitious products at the intersection of AI, fintech and modern engineering, this could be your next opportunity - Apply now!!!

Key requirements:

• 5+ years of professional experience building full-stack web applications in production.
• Strong frontend skills: TypeScript and React, with experience building production-grade UIs.
• Strong backend skills: Python (FastAPI, Django, or similar), with experience designing and building APIs and services.
• Practical experience designing and working with relational databases (PostgreSQL, MySQL) and writing efficient queries.
• Experience building and consuming REST APIs.
• Experience integrating AI/LLM-powered features into a product — calling LLM APIs, working with retrieval-augmented generation (RAG) or vector search, or partnering with ML/AI teams to ship model-backed functionality.
• Familiarity with cloud infrastructure (Azure, AWS, or GCP), CI/CD pipelines, and containerization (Docker).
• Strong understanding of software engineering fundamentals: testing, version control, code review, and system design.
• Proficiency working with AI coding assistants (e.g., Claude Code, GitHub Copilot, Cursor) to accelerate development, while critically reviewing and validating AI-generated code.
• Comfort working in a remote, async-first environment with strong written communication skills.

Great if you also have:

• Familiarity with infrastructure-as-code (Terraform) and observability tooling (Datadog, Grafana, Sentry).
• Familiarity with vector databases or embedding-based search for AI-powered features.
• Background working in a startup or small, cross-functional team.
• Exposure to design systems and component libraries.