Senior AI Software Engineer - Midrand / Hybrid - R900K PA

We are looking for a highly experienced Senior AI Software Engineer to help design, build, and scale production‑grade AI‑enabled software systems within a modern engineering environment.

This role is suited to a senior individual contributor who thrives in deeply technical environments and is passionate about engineering quality, platform reliability, distributed systems, and pragmatic AI integration. You will work closely with engineering leadership, platform teams, and security stakeholders to deliver resilient, observable, and secure software systems that operate at enterprise scale.

Key Responsibilities

• Design, build, and maintain scalable backend services and AI‑enabled applications.
• Develop secure, observable, and production‑ready distributed systems.
• Contribute hands‑on across architecture, implementation, code review, testing, and operational readiness.
• Design and evolve APIs, service integrations, and message‑driven workflows.
• Collaborate with platform, infrastructure, security, and engineering teams to deliver reliable software solutions.
• Drive engineering best practices across:
  • CI/CD
  • Testing automation
  • Observability
  • Security engineering
  • Release governance
• Participate in technical design discussions and provide mentorship to engineering teams.
• Translate complex technical and AI engineering decisions into clear technical documentation for engineering, security, and audit stakeholders.
• Support continuous improvement initiatives across engineering quality, reliability, and delivery processes.

Essential Engineering Experience

• 8+ years of hands‑on software engineering experience, including experience operating at Senior, Staff, or Principal Engineer level.
• Proven experience shipping production systems that other engineers and teams depend on.
• Deep proficiency in at least one modern backend language such as:
  • Python
  • TypeScript / Node.js
  • Java
  • Go
  • AI/ML platform integration
  • LLM application development
  • Retrieval‑Augmented Generation (RAG)
  • Vector databases
  • Kubernetes / Docker
  • Cloud platforms (AWS preferred)
  • Terraform / Infrastructure‑as‑Code

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