Engineering High-Trust
Developer Platforms
& Production GitOps.
Senior Infrastructure & Platform Engineer treating internal developer experience as a first-class product. Specializing in multi-tenant Kubernetes (EKS), modular Terraform, zero-downtime GitOps sync loops (ArgoCD), and safe agentic AI workflows that eliminate operational toil.
dev-mesh $ backstage-cli create --template=go-microservice
[07:51:02] Injecting modular Terraform manifests (AWS EKS VPC & IRSA)...
[07:51:03] Binding least-privilege IAM roles for service accounts...
[07:51:04] Auto-registering GitOps sync loop in ArgoCD...
[07:51:05] Provisioning KEDA auto-scaling metrics & Datadog monitors...
GITOPS ✔ Cluster target: eks-prod-useast1 synced (revision: a4f91)
HEALTH ✔ Canary route live at /healthz (200 OK)
⚡ Provisioning complete in 4m 12s. Manual Jira ticketing eliminated (was 5 business days).
incident-oncall $ kubectl get pods -n core-banking --field-selector=status.phase!=Running
NAME READY STATUS RESTARTS AGE
payment-gateway-7f89b-9x2pq 0/1 CrashLoopBackOff 4 (2m ago) 8m
incident-oncall $ k8s-triage diagnose payment-gateway-7f89b-9x2pq
[TRIAGE] Root cause isolated: ConnectionRefused on downstream RDS secret rotation.
[RUNBOOK] Executing verified non-mutating canary replay runbook #042...
✔ Zero downtime maintained. Runbook pushed to team knowledge base.
agentic-ops $ ai-incident-auditor correlate --since=15m
[AUDIT] Ingesting read-only streams: Datadog APM + CloudWatch + Pod Events
[CORRELATE] Error anomaly detected in egress NAT gateway packet drop (AZ-1a)
GUARDRAIL Deterministic Policy: AI mutation locked. State changes require GitOps PR.
✔ Incident timeline generated in 42s. Shared to incident channel.
How I Build Platforms & Infrastructure
Reliability and developer velocity are not opposing forces. When systems are designed with high trust, transparency, and self-service guardrails, engineering teams thrive asynchronously.
Infrastructure-as-a-Product
I treat internal software engineers as my primary customers. Rather than forcing developers to submit tickets or learn complex cloud abstractions, I build composable self-service developer platforms with sensible defaults and clear boundaries so product engineers can ship autonomously.
Async-First & Blameless Runbooks
True system reliability is about eliminating single points of failure. In distributed, high-context teams, a production incident isn't resolved until the runbook is documented, postmortems are shared blamelessly, and any on-call engineer can safely reproduce or mitigate the issue without heroics.
AI for Read-Only Toil; Deterministic GitOps for State
AI agents excel at correlating logs, identifying metric anomalies across Datadog/CloudWatch, and summarizing incident timelines. But state mutation—such as terraform apply or IAM grants—must remain deterministic, peer-reviewed, and securely gated through GitOps.
Mission-Driven Platform Reliability
When a platform serves thousands of small business owners and creators whose income depends on their scheduled publishing and analytics, uptime isn't an abstract metric—it directly protects someone's livelihood. Systems must be stable, observable, and built for intentional, long-term health.
The Self-Service Internal Developer Platform
How I architected an automated self-service platform at Analogue Shifts, transforming a 5-day manual environment provisioning bottleneck into a sub-5-minute automated GitOps loop.
Eliminating Manual Ticketing with Infrastructure-as-a-Product
Engineering teams faced friction spinning up new microservices. Provisioning required manual Jira tickets, bespoke Terraform manifests, and customized CI/CD pipelines. This caused configuration drift across environments and stretched new service onboarding time to roughly 5 business days.
Technical Implementation
- Single Interface: Centralized microservice creation in Backstage, removing cognitive load for product engineers.
- Parameterization: Modular Terraform blocks parameterized across staging and production to prevent infrastructure drift.
- Automated Rollbacks: Paired Argo Rollouts with canary releases to achieve zero-downtime deployments with instant rollback on error spikes.
Measurable Impact & Lessons
- 5 Days → <5 Minutes: New service creation and infrastructure provisioning time dropped by 99%.
- Zero Manual Tickets: Completely eliminated repetitive platform requests, freeing platform engineers for high-leverage architectural work.
- Key Insight: Internal platform adoption depends entirely on developer empathy. If an internal tool requires engineers to learn complex cloud abstractions before writing business logic, it will fail.
Professional Experience
A history of building resilient cloud systems, driving GitOps adoption, and mentoring engineering teams.
Founder & Principal Infrastructure Engineer
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Cloud Engineering & Orchestration: Deployed production-grade, highly available container workloads using Amazon Elastic Kubernetes Service (EKS), orchestrating microservices with Helm charts, and building auto-scaling triggers using Kubernetes Event-driven Autoscaling (KEDA).
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Toil Reduction via AI: Built and automated advanced infrastructure pipelines utilizing modern AI agents to accelerate debugging workflows, safely handle non-mutating system audits, and generate production-grade systems documentation built to stand up during critical incident responses.
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Platform Economics & Rightsizing: Implemented data-driven visibility structures across AWS, GCP, and Datadog, applying granular FinOps rightsizing practices that allowed system performance to scale linearly while bending the cloud spend curve down.
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Tech Ecosystem Creation: Founded and engineered EarnWithDevOps (ewd.alamztech.com), an interactive learning platform that uses isolated container sandboxes and automated terminal grading to scale global DevOps and cloud engineering talent.
DevOps Engineer / Infrastructure Engineer
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Infrastructure-as-a-Product: Designed and architected an internal self-service developer platform using Backstage, Terraform, and ArgoCD that completely eliminated manual ticketing workflows. Authored composable software templates that scaffolded microservice repositories, injected modular Terraform manifests, and auto-registered GitOps sync loops, reducing developer onboarding cycle time from 5 days to under 5 minutes.
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Production GitOps Delivery: Maintained day-to-day platform reliability for distributed applications, transitioning legacy delivery systems into automated GitHub Actions and GitOps architectures with zero-downtime rollback pipelines.
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Kubernetes Cluster Stability: Managed multi-tenant Kubernetes operations, drafting scalable Helm charts, optimizing resource quotas, and driving progressive deployment strategies to minimize drift across production, staging, and development loops.
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Incident Management Lifecycle: Handled distributed on-call shifts, taking point on live service disruptions, orchestrating rapid mitigations via incident.io/PagerDuty, and standardizing postmortem runbooks to eliminate repeating root causes and eradicate operational single points of failure.
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Automated Pipeline Testing: Constructed end-to-end integration and automated test workflows (including Playwright setups), reducing post-deployment runtime regression failures by 30%.
AI Trainer & Software Specialist
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Adversarial Agent Evaluation: Evaluated, stress-tested, and optimized AI-generated code outputs and reasoning models targeting highly complex software engineering and platform architecture prompt patterns.
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Technical Code Auditing: Conducted fine-grained evaluations of model explanations regarding cloud architecture, isolating subtle factual errors, tracking security misconfigurations, and validating code correctness.
Software Engineer Intern
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Developed backend software, maintained database schemas, and structured robust APIs with extreme focus on architectural scalability, maintainability, and production deployment readiness.
Software Engineer Intern
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Supported engineering teams in prototyping web services, assisting with code reviews, and configuring automated testing suites.
Sharing Knowledge in Public
I believe strongly in learning in public, demystifying distributed systems, and building educational tools that help the global developer community master real-world production engineering.
EarnWithDevOps
An interactive hands-on learning platform that uses isolated container sandboxes and automated terminal grading to train engineers on production incident response, CI/CD pipelines, and cloud infrastructure.
k8s-triage
An open-source hands-on repository providing misconfigured Kubernetes workload scenarios and manifests designed to teach engineers how to debug broken clusters through first-principles observability.
"The Pod That Won't Start: Debugging Kubernetes Live"
Speaker at DevFest Abuja. Instead of standard slide decks, this masterclass features live-debugging broken Kubernetes clusters on-stage alongside attendees to demonstrate real production triage techniques.
KCNA & CKAD Cohort Mentorship
Mentored cohorts of engineers preparing for the Kubernetes and Cloud Native Associate (KCNA) and Certified Kubernetes Application Developer (CKAD) certifications, focusing on practical terminal muscle memory over rote test memorization.
Skills & Technologies
Tools, protocols, and architectural paradigms I use daily to build and maintain high-reliability cloud platforms.
Certifications & Training
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Google Associate Cloud Engineer (ACE) – Google Cloud
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Microsoft Certified: Azure Fundamentals – Microsoft
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Advanced Business Foundations – Nexford University
Education
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BEng, Computer Engineering
University of Ilorin, Nigeria (Graduated) -
Lifelong Learning in Public
Active contributor to cloud-native open source & hands-on workshops
Let's Build Resilient Systems Together
Whether you are building an async-first developer platform, looking for a senior platform engineer who treats internal teams as customers, or scaling reliable cloud infrastructure.