Software & AI Engineer • OCR/CV • RAG • Backend
I build production AI systems—document automation, retrieval, and backend services—designed for reliability.
Based in Cape Town • Open to hybrid/remote roles
References available on request.
Public proof is available via case studies, repos, and certifications.
Credentialed programs and core platforms used in delivery.
Production AI, backend, reliability, and embedded systems.
Consolidated view of the tools used across projects.
Node.js
Java
Spring Boot
Express.js
Fastify
PostgreSQL
MySQL
Supabase
Pandas
NumPy
Vercel Edge
Cloudflare R2
Stripe
Docker
Google Cloud
Terraform
GitHub Actions
Git
Linux
OpenTelemetry
Testing Library
npm
ESLint
Postman
cURL
Copilot CLI
Rust
WASM
JavaScript
Featured picks first; filter for detail.
Validation-first OCR pipeline with explainable outputs.
Grounded retrieval with reproducible indexing and guardrails.
End-to-end training and evaluation pipeline.
Production OCR + LLM pipeline for document-to-structured-data automation.
RAG assistant for portfolio and candidate Q&A with grounded retrieval and conservative response behavior.
Sensor fusion + edge-AI foundations for real-time hazard detection.
Ongoing work on an AI-driven product with details limited by NDA.
Note: Details limited by NDA; happy to discuss at a high level.
Neural network image classifier with an end-to-end training and evaluation pipeline.
Full-stack web app emphasizing usability, data integrity, and maintainable APIs.
Hands-on systems work: scheduling simulations, protocol design, and integrity checks.
A small selection of public work you can review immediately. For private/NDA work, I share high-level details and can walk through specifics live.
A quick view of how my work evolved across domains. Details are intentionally high-level where needed.
Verified cloud learning and hands-on labs focused on practical, production-relevant skills.
Selected programs that strengthened leadership, communication, and technical execution.
Short, public-safe notes on how I build and ship reliable systems.
In production OCR, errors often look “plausible” and silently poison downstream systems. My default is validation-first extraction: explicit constraints, cross-field rules, confidence thresholds, and human-review hooks. The goal is not just extracting text, but producing outputs you can trust and audit.
A good RAG system is as much about “when to refuse” as it is about retrieval. I focus on deterministic indexing, conservative thresholds when evidence is weak, and clear provenance (links back to sources). This keeps the assistant helpful while staying honest and grounded.
What helps me move fast safely: reproducible environments, dependency hygiene, automated checks, and clear “definition of done”. Even lightweight CI, good error handling, and predictable releases reduce firefighting and increase delivery cadence.
I build end-to-end AI features that hold up in production: ingestion, validation, retrieval, model integration, observability, and delivery. My focus is correctness and reliability: clear interfaces, defensive checks, and measurable outcomes.
Software engineering, AI/ML engineering, and backend/cloud roles. Open to hybrid or remote opportunities.
University of Cape Town
Computer Science & Computer Engineering
Email or LinkedIn preferred. Resume and GitHub below.
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