Design for operations
I think about system health, monitoring, failure handling, service ownership, incident response and useful audit trails from the beginning of a project.
Cloud Engineering · Platform Engineering · DevOps
Building cloud platforms that actually operate.
I'm Olawale Azeez, a Cloud & Platform Engineer designing, automating, securing and operating cloud-native systems across AWS, Azure and Kubernetes. I focus on engineering that can be deployed, validated, observed and explained.
engineering-profile
$ cat engineer.yaml
# Engineering focus
role: cloud-platform-engineer
cloud:
- aws
- azure
platform:
- kubernetes
- terraform
- backstage
delivery:
- github-actions
- gitops
- workload-identity
operations:
- observability
- ai-assisted-workflows
status: building-and-improving ✓
Cloud deployment evidence
AWS and Azure architecture supported by
infrastructure code and validation records.
Security-first engineering
Identity, RBAC, private networking,
container hardening and access boundaries.
Automated delivery
CI/CD, infrastructure validation,
automated tests and GitOps workflows.
Operational thinking
Monitoring, tracing, incident evidence,
approval gates and auditability.
01 / Engineering philosophy
Beyond the architecture diagram.
Cloud platforms should not stop at successful deployment. They must be secure, testable, observable, maintainable and understandable to the engineers responsible for running them.
Automate the repeatable
I use Infrastructure as Code, CI/CD, GitOps and developer self-service to reduce manual configuration and promote consistent engineering workflows.
Make engineering verifiable
I document decisions and build validation into delivery pipelines. Tests, code, diagrams and deployment evidence should support the claims made about a system.
02 / Selected engineering work
Flagship case studies.
Four publicly inspectable projects demonstrate how I approach platform architecture, delivery, security and operations. CalcioSapien, a separate private product spotlight below, illustrates evidence-led domain engineering and governance.
Kubernetes / Platform Operations
CASE STUDY 01
PlatformPilot
An AI-assisted Kubernetes operations platform connecting cluster visibility, workload diagnostics, Prometheus monitoring, role-based access and structured operational findings.
The engineering problem
Engineers need actionable context when investigating unhealthy workloads. Kubernetes resources, events, logs and metrics are often explored separately, increasing the effort required to understand a cluster incident.
Architecture and implementation
PlatformPilot uses a React frontend, FastAPI backend, Kubernetes Python Client and Prometheus integration. Resource-focused APIs support cluster navigation, while operational analysis adds contextual findings and guidance.
Engineering decisions
Protected API access uses viewer, operator and admin roles. Structured operational findings can be exported to CloudOps Command Center, keeping observation and controlled remediation separate.
Kubernetes
FastAPI
React
Prometheus
Python
RBAC
Operational Reliability / Controlled Automation
CASE STUDY 02
CloudOps Command Center
An infrastructure operations workspace that transforms incoming risks into evidence-backed, owner-routed investigations with explicit human approval boundaries.
The engineering problem
Operational alerts alone do not explain impact, ownership, remediation requirements or the evidence needed to make a safe decision.
Architecture and implementation
A Next.js and TypeScript application integrates infrastructure signals, risk classification, runbooks, service ownership and persistent operational workflows. Structured findings from PlatformPilot extend the investigation flow.
Engineering decisions
The deterministic Decision Trace models a sequence of premise, reasoning, hypothesis, verification and conclusion. It keeps raw evidence separate from interpretation, and does not itself authorize remediation.
Next.js
TypeScript
PostgreSQL
GitHub Actions
Risk Analysis
Auditability
Full-Stack SaaS / AI-Assisted Operations
CASE STUDY 03
OpsPilot SaaS
A multi-tenant operations platform that turns business signals into prioritized recommendations, human-approved actions and tracked execution workflows.
The engineering problem
Small businesses may receive operational signals through email, notes, customer interactions and disconnected processes. Turning those signals into accountable action is difficult.
Architecture and implementation
The application uses Next.js, TypeScript, PostgreSQL and server-side authorization. Its SaaS architecture includes tenant workspaces, operational ingestion, approval queues, execution records and billing foundations.
Engineering decisions
AI-assisted recommendations are separated from business decisions. The platform implements human approval boundaries, permission checks and execution-state tracking instead of assuming that model output should directly trigger actions.
Next.js
TypeScript
PostgreSQL
Clerk
Stripe
AI Workflows
Vercel
Microsoft Azure / Secure Application Platform
CASE STUDY 04
PropertyOps AI Agent
An Azure-hosted property operations platform demonstrating secure cloud deployment, maintenance workflows, identity integration, private networking and infrastructure automation.
The engineering problem
Operational applications require more than business logic. They also need secure identity, controlled access to services, repeatable deployment and visibility into application behaviour.
Architecture and implementation
PropertyOps combines FastAPI, PostgreSQL, Azure Container Apps, Microsoft Entra ID, managed identities, Key Vault, Application Insights and modular Bicep infrastructure.
Engineering decisions
Application-role authorization protects operations, private endpoints isolate supported data services and GitHub OIDC enables deployment without storing long-lived Azure client secrets.
Azure
FastAPI
Bicep
PostgreSQL
Entra ID
Private Link
OIDC
Private product · Independent engineering project
Football intelligence & decision governance
CalcioSapien
A private football intelligence platform designed to help a sporting department move from sourced player evidence to accountable recruitment decisions.
The product connects data ingestion and provenance, scouting observations, recruitment shortlists, scenario analysis, board authorisation, negotiation workflows and audit history. The emphasis is on explaining why a decision is supported and who is authorised to make it.
I developed a FastAPI and React application with persistent workflows, role-scoped permissions, session controls, backend tests, CI checks and documented private-hosting, backup and recovery procedures. Analytical capabilities distinguish deterministic scoring from opt-in model-assisted evidence selection.
Python / FastAPI
React
Data provenance
Decision workflows
Role-based access
Docker / CI
Enquire about a private walkthrough ↗
Public-safe overview only. Source code, licensed datasets, operator accounts and deployment details are intentionally not published here. This is a developing product, not a claim of commercial adoption or production certification.
03 / Engineering workflow
From design to reliable operations.
A repeatable approach connecting architecture, infrastructure, automated checks, delivery and ongoing operational visibility.
STEP 01
Design
Architecture, boundaries, risks and decisions
STEP 02
Provision
Terraform, Bicep, AWS SAM and cloud resources
STEP 03
Validate
Tests, linting, schema checks and security scans
STEP 04
Deliver
GitHub Actions, containers, OIDC and GitOps
STEP 05
Operate
Metrics, logs, tracing, alerts and evidence
04 / More engineering projects
Explore the engineering portfolio.
Supporting projects organized by engineering discipline. These supporting projects complement the flagship case studies and the private CalcioSapien product spotlight.
Cloud Infrastructure & Security
Provisioning · workload security · cloud platformsPharmacy API on Amazon EKS
FastAPI workload with Amazon Cognito, EKS Pod Identity, DynamoDB, Kubernetes resilience controls, CI/CD and container supply-chain validation.
Explore repository ↗Terraform AWS EKS Platform
Modular Terraform infrastructure, private networking, automated guardrail tests, IaC scanning and AWS plan workflows authenticated with OIDC.
Explore repository ↗AZ-104 Azure Infrastructure Lab
Modular Bicep, private virtual machines, Azure Bastion, managed identities, RBAC, Private Link, backup and deployment verification.
Explore repository ↗Developer Platforms & Delivery
Self-service · CI/CD · GitOpsBackstage Developer Portal
Backstage software catalog, governance, reusable service templates, golden paths, permissions and delivery standards.
Explore repository ↗Platform Engineering CI/CD
Container validation, vulnerability scanning, AWS ECR publishing, GitHub OIDC, software bills of materials and artifact attestations.
Explore repository ↗Argo CD GitOps Platform
Declarative Kubernetes delivery, pull-request validation, Argo CD reconciliation and application deployment configuration.
Explore repository ↗Backstage Generated Inventory API
An example FastAPI service generated from the Backstage golden path, including container support, Kubernetes resources, CI and tests.
Explore repository ↗Observability & AI Engineering
Telemetry · retrieval · automationPlatform Observability Stack
Prometheus, Grafana, OpenTelemetry, Loki, Tempo, SLO monitoring, error-budget alerts and automated rule validation.
Explore repository ↗RAG Ops Assistant
Grounded operations assistant with retrieval, Amazon Bedrock, LangGraph, MCP and AWS ECS Fargate deployment.
Explore repository ↗AI Resume Analyzer
Resume evidence extraction, explainable skill scoring, n8n workflows and AWS Bedrock career analysis.
Explore repository ↗AI Content Automation Platform
AWS Lambda and SAM-based workflow for content planning, approvals, OAuth connection, scheduling and X publishing automation.
Explore repository ↗Applications & Engineering Knowledge
Serverless applications · evidence · documentationCloud-Native Pharmacy Platform
Pharmacy inventory application with a React dashboard, AWS Lambda functions, tenant-aware workflows, event processing and automated tests.
Explore repository ↗Cloud & Platform Engineering Handbook
A published, project-led reference with hands-on labs covering AWS, Azure, Kubernetes, Terraform, GitOps, SRE, secure AI operations, architecture and technical interviews. Includes automated documentation and security quality checks.
Read the handbook ↗
05 / Verifiable engineering
Explore the evidence behind the work.
I aim to support project descriptions with implementation, tests, configuration and deployment records. These links let you inspect those artifacts directly.
01 — AWS workload security
Examine Cognito token handling, Kubernetes workload controls, identity integration and CI/CD.
Inspect EKS evidence ↗02 — Azure infrastructure
Review modular Bicep, private endpoints, managed identities and documented Azure validation screenshots.
Inspect Azure evidence ↗03 — Observability validation
Examine telemetry configurations, Prometheus alert rules, dashboards, automated rule tests and smoke checks.
Inspect reliability evidence ↗04 — Infrastructure testing
Review reusable Terraform modules, guardrail tests, static validation and security workflows.
Inspect Terraform tests ↗05 — Application authorization
Explore protected APIs and security testing across Kubernetes operational services.
Inspect access-control tests ↗06 — Controlled automation
Explore risk validation, decision traces, human approval boundaries and operational workflow testing.
Inspect operational tests ↗Repository artifacts and screenshots demonstrate particular implementation and validation steps; they do not imply continuous production availability or independent certification.
06 / Technical capabilities
Core engineering stack.
Technologies used across infrastructure, applications, platform delivery and operational engineering projects.
Cloud Platforms
Amazon Web Services, Microsoft Azure, EKS, ECS Fargate, Lambda, Azure Container Apps, IAM, Microsoft Entra ID, Key Vault.
Infrastructure as Code
Terraform, Azure Bicep, AWS SAM, CloudFormation, networking, private endpoints, remote state and configuration validation.
Containers & Kubernetes
Docker, Kubernetes, Helm, Argo CD, deployment manifests, workload identity, RBAC, runtime hardening and health probes.
CI/CD & DevSecOps
GitHub Actions, GitOps, GitHub OIDC, Trivy, TFLint, IaC scanning, SBOMs, software supply-chain controls.
Observability & Reliability
Prometheus, Grafana, OpenTelemetry, Loki, Tempo, Alertmanager, CloudWatch, Application Insights and SLOs.
Software & AI Systems
Python, FastAPI, TypeScript, React, Next.js, PostgreSQL, Amazon Bedrock, RAG, LangGraph and MCP.
07 / Continued development
Learning backed by practical implementation.
My approach to professional development combines formal learning with applied engineering projects, troubleshooting and documented technical decisions.
AWS Certified Developer – Associate
AWS application development, services, deployment and cloud engineering foundations.
Azure Infrastructure Engineering
Hands-on AZ-104-oriented labs covering network security, compute, identity, storage, backup and Bicep deployment.
View Azure lab ↗Engineering Handbook
Technical concepts connected to working implementations, engineering decisions, troubleshooting and lessons learned.
Read published handbook ↗
08 / Connect
Interested in building reliable cloud platforms?
I'm open to opportunities involving Cloud Engineering, Platform Engineering, DevOps, infrastructure automation, developer platforms and reliability engineering. I welcome conversations about engineering roles, technical projects and collaboration.