$ inrun inspect platform-database:v2.1.0
Alex Eze

Alex.Eze

// Platform Engineer · Creator of Inrun

I build infrastructure and platforms for Kubernetes-native teams — and I'm building Inrun, a runtime that separates Kubernetes reconciliation behaviour from the infrastructure that executes it. Bring your intent and your logic; Inrun manages the reconciliation machinery.

Inrun

A Kubernetes reconciliation runtime built around one idea: separate application-specific behaviour from the infrastructure required to execute it.

Pre-release · Targeting v0.1.0

Separate reconciliation behaviour from operator infrastructure.

Kubernetes already provides a battle-tested reconciliation model, but using it traditionally means building and maintaining controllers, watches, queues, retries, status handling, and cleanup around application-specific logic. That machinery is often repeated even when the decision being made is completely different.

Inrun makes the machinery reusable. Supply an intent and implement the decision using declarative rules, a remote HTTP endpoint, or typed Go reconciliation. Inrun prepares the request, executes the behaviour, applies permitted resources, and manages the reconciliation lifecycle. The caller-facing intent can remain separate from the internal CRD representation.

Declarative Reconciliation behaviour
Remote HTTP-based reconciliation
Typed Go Full reconciler control
Apache 2.0 License
⚡
Choose your reconciliation model
Use declarative behaviour, a remote HTTP reconciler in your language of choice, or a typed Go reconciler. Keep application decisions separate from shared runtime machinery.
🔒
Runtime-owned lifecycle
Inrun owns the informers, queues, retries, leader election, status updates, resource application, and cleanup surrounding reconciliation.
📦
Intent and representation
Keep caller-facing intent separate from CRD storage shape, with catalog-driven field mapping and schema migration support.
🧪
OCI-native behaviour artifacts
Package and distribute behaviour through OCI, with artifact metadata and verification as part of the development and delivery workflow.
Related library · KEL

Kubernetes Expression Library

KEL is the reusable expression-function layer behind Inrun's templates: pure, nil-safe helpers for inspecting Kubernetes objects, working with fields and collections, validating values, and expressing common operational logic. The aim is to make that Kubernetes knowledge useful outside the Inrun runtime too.

Where I've worked

Infrastructure Developer (AI/ML) 2025 · Present
GOSH NHS · London, UK
  • Designed and deployed hybrid Kubernetes clusters for ML workloads across AWS and on-premises infrastructure.
  • Built MLOps pipelines on AWS SageMaker to automate model lifecycle management.
  • Architected multi-environment agentic workflows using AWS Strands and Bedrock.
  • Tuned resource allocation and networking for optimised AI/NLP workloads across hybrid environments.
MLOps Engineer 2025 · Consultant
Kredete · United States
  • Designed a multi-tenant platform for rapidly deploying machine learning applications through configuration-driven templates.
  • Architected agentic workflows using the latest AWS Agentic platform — AgentCore.
  • Set up MLflow for the full ML lifecycle: manageable, traceable, and reproducible across all project phases.
  • Delivered a fully automated, production-ready, GitOps-driven Kubernetes environment aligned with SOC 2 and ISO 27001 requirements.
Lead Platform Engineer 2025 · Consultant
OnDgo
  • Rebuilt GCP infrastructure from scratch using Terraform, Vault, and ArgoCD with zero downtime migration.
  • Reduced public IP usage by 85% via secure ingress consolidation and private cluster networking.
  • Enabled GitOps across multi-cluster Kubernetes with centralised monitoring, progressive delivery, and dynamic secret injection.
  • Led platform hardening: RBAC, workload identity, disaster recovery, and event-driven autoscaling.
Lead DevOps Engineer 2024
Interswitch Group
  • Introduced DevOps culture and practices across engineering teams for the first time.
  • Automated infrastructure provisioning and deployments across 20+ applications.
  • Cut release cycles by 85% and achieved annual cost savings of $10K+.
Senior DevOps Engineer 2022 – 2024
Andela
  • Founded the DevOps/Platform team, building cloud architecture and CI/CD from the ground up.
  • Enabled infrastructure to handle 20× traffic growth through autoscaling and container orchestration.
  • Reduced release times by 75%. Mentored junior engineers, boosting team productivity by 80%.
Full experience on LinkedIn →

What I work with

Expert — daily use, deep knowledge

Kubernetes Go Inrun KEL Terraform ArgoCD GitHub Actions Helm Prometheus + Grafana Vault AWS GCP

Proficient — regular use

Flux CD Istio Kyverno Velero External Secrets Operator Argo Rollouts Azure Ansible MLflow SageMaker Kafka Redis Python Bash

Familiar — used in projects

Crossplane Falco Chaos Mesh KubeFlow Rust OpenTofu Keycloak
BSc — University of Benin Kubernetes for Developers — Linux Foundation Lean Six Sigma Green Belt CS50 — Harvard University DevOps & Cloud Engineering — IReign

Get in touch

Open to platform engineering roles and collaborations around Kubernetes infrastructure and Inrun.

ialexeze@gmail.com