Platform Engineering

Reliability-First
Automation & Infrastructure

View Platform Cases GitHub

The Platform Journey

I started in cloud and DevOps automation, working hands-on with Linux servers, containerized services, and CI/CD pipelines.

Over time, my work shifted toward building Internal Developer Platforms (IDPs) and self-service automation. I focused on enabling non-technical teammates to utilize AI-powered workflows without needing to touch infrastructure.

Certifications

● AWS Cloud Practitioner ○ AWS Solutions Architect (Target: Q1 2026)

What I Build

Internal Developer Platforms

Self-service platforms that abstract complexity and provide repeatable ways to deploy services.

Cloud Infrastructure

Secure, scalable cloud environments designed for reliability, maintainability, and growth.

Automation & Reliability

Systems designed to be observable, debuggable, and "boring" in production.

Professional Experience

Platform & Automation Engineer

Excellessence (YourEKA Services)

Jul 2025 – Sep 2025

Harare, Zimbabwe · Hybrid

Designed and operated a container-based internal platform supporting AI-driven demos and automation tools.

Responsibilities
  • ▹ Deployed and hardened Linux infrastructure with secure defaults.
  • ▹ Built self-service automation workflows enabling non-technical users to trigger AI pipelines.
  • ▹ Defined environment variable and secrets patterns for security.
  • ▹ Diagnosed and resolved container scheduling and configuration issues.
Selected Outcomes
  • ✓ Reduced automation pipeline downtime by ~60%.
  • ✓ Delivered stable platforms used in live client demos.
  • ✓ Improved consistency of AI outputs through enforced schemas and parsing logic.

Core Capabilities

Cloud & Infrastructure

AWS (EC2, S3, RDS) Terraform (IaC) Route 53 API Gateway Linux Hardening

Orchestration

Docker Docker Compose Kubernetes (k3s) Rancher Desktop

Platform Automation

GitHub Actions n8n Workflow Bash Scripting YAML Pipelines

Reliability & Ops

Logging Setup Incident Response Root-Cause Analysis Debugging

Security & Governance

IAM Policies Secrets Handling SSH Key Auth Firewalls

AI Platform Add-Ons

Whisper API Gemini / LLMs Qdrant (Vector DB)

Workflows & Automation Systems

I design and build automation systems that turn messy operational data into clear, actionable intelligence. These workflows are built for real businesses, real teams, and real decisions — with reliability, safeguards, and scale in mind.

Flagship Case Study

AI-Powered Delivery Health & Operations Intelligence System (n8n)

A production-grade automation system that monitors delivery health across multiple projects, applies objective scoring, uses AI for diagnostics, and delivers consolidated, actionable reports for leadership.

Business AI · Internal Tools · Intelligent Automation
Delivery Health & Operations Intelligence demo

Business Context

  • • Multiple projects running simultaneously with fragmented visibility
  • • Dashboards often inaccurate or biased by manual inputs
  • • Decisions based on gut-feel, with no clear escalation paths
  • • Naïve alerting leads to alert spam and ignored signals

System Capabilities

  • • Project health scoring (objective, repeatable)
  • • Status classification (Red / Yellow / Green)
  • • AI-generated key issues and prioritized recommendations
  • • Aggregated alerts with anti-spam logic
  • • Manual review escalation for edge-cases
  • • Verified updates back into ClickUp (two-way sync)
Architecture & Reliability

Anti-duplication logic, AI retry handling with bounded backoff, strict validation before execution, and human-in-the-loop safeguards ensure this is a reliable operational system — not a brittle demo.

Outcome: Built as a real pilot assignment and shortlisted in a senior-facing evaluation. Demonstrates system thinking, not scripting.
Watch full demo on YouTube

Platform Case Studies

Architecture, Standards, and Value Delivery

Acquisitions — Platform for Buying & Selling SaaS Businesses

Platform · API

Overview: Built and deployed a production-ready API to buy and sell SaaS businesses: authentication, RBAC, listings, deal workflows, observability, and a full DevOps lifecycle.

Key Features

  • • JWT-based authentication and authorization with role-based access control (admins, users).
  • • User account management and business listings (create, update, delete, browse).
  • • Deal management to track deals from pending → completed with state transitions.
  • • Request validation using Zod and structured logging via Winston.
  • • Health monitoring for endpoints and operational metrics collection.

DevOps & Tech Stack

  • • Git with branching strategy, code reviews, and GitHub-hosted repository.
  • • CI/CD with GitHub Actions (lint, test, build, deploy gates).
  • • Docker for containerization and Kubernetes for orchestration with rolling deployments.
  • • Infrastructure as Code on AWS; Neon DB (serverless Postgres) + Drizzle ORM.
  • • Security: Arcjet for bot/spam protection; JWT and RBAC for access control.
  • • Monitoring & Logging: Winston, Prometheus, Grafana for dashboards and alerts.
  • • Code quality & testing: ESLint, Prettier, Jest, Supertest.
  • • Developer workflow: Warp and AI-assisted workflows for productivity.

Outcome: Production-ready API demonstrating end-to-end platform delivery, observability, security, and automation for SaaS M&A workflows.

View Repository Request case details
Node.js JWT Zod Winston Docker Kubernetes GitHub Actions Neon Drizzle Prometheus Grafana Jest

Voice-to-Vector API

Internal API

A Flask-based AI system that transforms voice notes into structured insights and stores them in a vector database for semantic retrieval.

Components:

  • • End-to-end intelligence pipeline.
  • • Vector storage using Qdrant.
  • • Integrates transcription and LLM reasoning.

Value: Exposed AI capabilities as reliable internal services, not just experiments.

View Repository
Flask Qdrant Docker

Managed Legacy Migration

Modernization

Overview: Migration of a legacy Java application from manual server management to a managed AWS platform.

Platform Approach:

  • • Elastic Beanstalk for orchestrated deployment
  • • RDS & ElastiCache for managed data layers
  • • Documented a "Golden Path" for onboarding

Value: Drastically reduced operational overhead and improved scalability.

Elastic Beanstalk RDS

Serverless App Pattern

IaC & CI/CD

A reusable serverless application platform pattern built using AWS managed services. This portfolio Website runs on this pattern.

Highlights:

  • • Infrastructure entirely defined with Terraform
  • • Secure-by-default and fully repeatable
  • • Automated GitHub Actions CI/CD pipeline

Value: Standardizes lightweight API and static application deployments.

Terraform Lambda GitHub Actions

Looking for a Platform Engineer?

I am actively seeking Junior Platform Engineer or Cloud Platform Engineer roles. I thrive in environments that value reliability, clear standards, and steady iteration.

Reach Out

Let's Connect

Open for collaborations and platform opportunities

tfrsuperfx@gmail.com
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