AI Engineer - Agentic AI, RAG & LLM backends in Python. I build AI systems for environments where mistakes are expensive: insurance, claims, and regulated financial workflows.
My background is regulated funeral-assurance and financial-services engineering - multi-step digital application flows, claims-workflow automation, and compliance-aware backends. That shaped how I build AI: every model call is a fallible dependency, every run is auditable, every pipeline is a contract.
📍 Zimbabwe · South Africa (remote-first) · 📧 mikencube03@gmail.com 🔗 Portfolio · LinkedIn
AI/LLM - Agentic workflows (planner/executor/critic), RAG, structured outputs, eval harnesses · LangChain / LangGraph · OpenAI / Anthropic Backend - Python · FastAPI · Flask · Pydantic · REST/OpenAPI-first Data - Contract-first ETL · drift detection · PostgreSQL · pgvector · Pandas / Polars Cloud & infra - AWS (S3, Lambda, SQS, RDS) · Docker · Redis
Each repo is labelled with its scope -
production,reference architecture, orlearning lab- so you know exactly what you're looking at.
| Project | What it is | Status |
|---|---|---|
| proactive-sentinel | Multi-tenant security/monitoring backend — JWT auth, encrypted PII, Postgres, Dockerised, tested | 🟢 Shipped |
| digi-app-form | Multi-step digital application system — captures, validates & processes client submissions for a regulated funeral-assurance workflow | 🟢 Shipped |
| End-to-End-Recommender-System-Data-Pipeline | Contract-first data pipeline feeding a recommender, with validation at each hop | 🔵 Reference |
| Resilient-Web-Tier-on-AWS-ALB-Auto-Scaling | AWS Well-Architected demo — ALB + Auto Scaling across reliability, security, cost | 🟡 Lab |
Applied / Agentic AI Engineer · AI Platform Engineer · Data Engineer (AI systems) - on a team putting AI into high-stakes, regulated, or operationally serious products.
If that's your team, let's talk.