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DevOps Engineering · Murcia, Spain

Javier Jiménez Molina

DevOps Engineer

Growing toward Platform & Cloud Engineering

CI/CD, Kubernetes and automation for reliable delivery and operations.

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profile / current focus

javier@platform:~$ summary --current

  • Delivery pipelines with Jenkins.
  • Kubernetes and production support.
  • I audit and optimize resources.

Technologies with applied experience

  • Kubernetes
  • Jenkins
  • Docker
  • Helm
  • Linux
  • Groovy
  • Git / Bitbucket
  • Prometheus
  • Grafana
  • Bash
  • React
  • TypeScript

The person behind the terminal

About me

I'm Javier, a DevOps Engineer based in Murcia. I started out building applications and became interested in everything that makes them reliable to deliver and operate: pipelines, containers, observability and automation.

I like understanding the whole system, investigating difficult problems and turning repetitive tasks into clearer processes. Outside work I build personal products and explore how AI can support engineering work.

Personal work

Projects

A gallery of the personal products, platforms and tools I have built or continue to develop.

Web and mobile product

MoneyFlow

A personal finance app with web and React Native clients. They share logic in a monorepo and use Supabase as the backend.

  • React
  • React Native
  • TypeScript
  • Supabase
  • PostgreSQL
  • Gemini API

Self-hosted platform

Self-hosted Knowledge Platform

An Outline-based documentation platform with custom extensions, operated on Linux and container infrastructure.

  • Linux
  • Docker
  • PostgreSQL
  • Redis
  • CI/CD

Product extension

Print Studio

An extension integrated with the knowledge platform to format documents and prepare them for printing or export.

  • Web development
  • Docker
  • CI/CD
Web and mobile product

MoneyFlow

A personal finance app with web and React Native clients. They share logic in a monorepo and use Supabase as the backend.

The web and mobile clients share logic in a core package within a monorepo, so product rules do not have to be duplicated across interfaces.

Supabase provides authentication, PostgreSQL and Edge Functions. The product includes accounts, transactions, transfers, recurring entries and analytics dashboards; Gemini assists natural-language entry.

What it includes

  • React web app and React Native mobile app
  • Shared core package in a monorepo
  • Supabase, PostgreSQL and Edge Functions
  • AI-assisted natural-language transaction entry

Technologies

  • React
  • React Native
  • TypeScript
  • Supabase
  • PostgreSQL
  • Gemini API
Self-hosted platform

Self-hosted Knowledge Platform

An Outline-based documentation platform with custom extensions, operated on Linux and container infrastructure.

An Outline-based self-hosted project: reverse proxy, application, PostgreSQL and Redis run in containers on Linux.

Alongside the deployment, I work on custom extensions and their change and CI/CD workflow. Print Studio belongs to the same ecosystem.

What it includes

  • Docker deployment and reverse proxy
  • PostgreSQL and Redis
  • Custom extensions and CI/CD workflow

Technologies

  • Linux
  • Docker
  • PostgreSQL
  • Redis
  • CI/CD
Product extension

Print Studio

An extension integrated with the knowledge platform to format documents and prepare them for printing or export.

An extension integrated into the knowledge platform to prepare and format documents for printing or export.

It is developed alongside the self-hosted application and shares its workflow: code changes, container deployment and CI/CD.

What it includes

  • Application customization
  • Containerized deployment
  • CI/CD integration

Technologies

  • Web development
  • Docker
  • CI/CD
 View all projects

Career path

Professional experience

My path connects software development with automation, continuous delivery and platform operations.

2025 — present

Krimda

Current

DevOps Engineer

CI/CD and Kubernetes operations, focused on automation, deployment standardization and production troubleshooting.

  • Designed and maintained modular Jenkins pipelines with Groovy Shared Libraries to standardize deployments and reduce duplication.
  • Administered and troubleshot Kubernetes; handled Docker and Helm deployments and production incidents.
  • Developed scripts to audit resources by microservice and inform optimization proposals.
  • Managed binaries and dependencies with JFrog Artifactory integrated into the Bitbucket workflow.
  • Used Grafana, Prometheus and Graylog dashboards, metrics and logs to improve operational visibility.

2021 — 2025

Capgemini

Software / DevOps Engineer

I started in Full-Stack development and gradually moved toward automation, CI/CD and DevOps.

Role progression

  1. Development
  2. Automation
  3. CI/CD
  4. DevOps
  • Developed Java and Spring Boot microservices, plus React and Node.js applications and REST APIs.
  • Built Jenkins and GitHub Actions pipelines while moving toward automation and CI/CD.
  • Worked with Docker, Kubernetes and Helm; monitored environments with Rancher and Lens.
  • Deployed applications and managed resources on OVHCloud.
  • CI/CD improvements reduced deployment times by more than 80%.

Day-to-day engineering

Concrete problems, practical solutions

Real cases from CI/CD, automation and platform operations: the problem, how I approached it and what it brings to the team.

A shared workflow for Python projects

A complete Python build and deployment workflow, reusable across projects through Jenkins Shared Libraries.

Configuration changes without rebuilding the application

Validation of changes to values.yaml, application.properties and other configuration files to avoid unnecessary Jenkins builds.

Pull Requests that reach the team automatically

Bitbucket webhooks connected to Jenkins to notify reviewers through Slack, email and Microsoft Teams.

From a Jenkins failure to a container in Kubernetes

Troubleshooting build, deployment and Docker container execution issues in Kubernetes.

HTTP 500 errors with direct Slack notifications

Grafana alerts backed by Prometheus metrics to bring server errors into the team's working channel.

Resource auditing by microservice

Scripts to review Kubernetes resources and inform optimization proposals for each microservice.

Faster delivery at Capgemini

CI/CD improvements that reduced deployment times by more than 80% during my transition toward DevOps.

Validating a Helm chart and its telemetry

Finding an invalid HPA configuration during a dry-run and preparing the chart fix and telemetry integration.

Diagnosing memory-related restarts in Kubernetes

Analysis of out-of-memory restarts and proposed requests and limits adjustments based on observed behavior.

CI/CD · Standardization

A shared workflow for Python projects

A complete Python build and deployment workflow, reusable across projects through Jenkins Shared Libraries.

Pipeline and Shared Library development

The challenge
Bringing Python into the shared delivery flow without creating and maintaining a separate pipeline for every project.
What I did
I developed a complete workflow for this new technology using Jenkins Shared Libraries, keeping reusable build and deployment logic in a common foundation.
What it brings
One maintenance point for Python projects and a delivery flow consistent with the team's other technologies.

Case technologies

  • Jenkins
  • Shared Libraries
  • Groovy
  • Python
CI/CD · Developer Experience

Configuration changes without rebuilding the application

Validation of changes to values.yaml, application.properties and other configuration files to avoid unnecessary Jenkins builds.

Development and validation

The challenge
A change to values.yaml or application.properties triggered a full build even when the application code had not changed.
What I did
I developed pre-validation in the Shared Library to classify files in the exact commit and skip unnecessary builds. Classification fails closed on errors. I tested it across several technology generators and in one Jenkins run.
What it brings
Less waiting for developers when adjusting configuration and less repeated work on Jenkins agents.

13–25 h / day

Potential aggregate savings · 67–125 h / week

Estimate for around 100 microservices (10 projects × 10): 4–5 weekly changes per microservice × 10–15 min per build, spread across 5 working days. This adds up CI agent execution time across projects, excluding deployments.

Case technologies

  • Jenkins
  • Shared Libraries
  • Git
  • Helm
Automation · Collaboration

Pull Requests that reach the team automatically

Bitbucket webhooks connected to Jenkins to notify reviewers through Slack, email and Microsoft Teams.

Integration developed

The challenge
The team needed to hear about Pull Requests without manually checking Bitbucket or sending a separate message to every channel.
What I did
I automated the Bitbucket → webhook → Jenkins flow and the subsequent notifications to the Slack channel, email and Microsoft Teams.
What it brings
Review requests reach the team's working channels, reducing the need to chase manual notifications.

Case technologies

  • Bitbucket
  • Webhooks
  • Jenkins
  • Slack
  • Email
  • Microsoft Teams
Operations · Troubleshooting

From a Jenkins failure to a container in Kubernetes

Troubleshooting build, deployment and Docker container execution issues in Kubernetes.

Incident resolution

The challenge
Delivery could stall during the build, deployment or container execution: each stage needed a different diagnosis.
What I did
I resolved build and deployment failures in Jenkins pipelines and Docker container issues in Kubernetes, following the failure from the pipeline run to the application's behavior.
What it brings
Restoring the delivery flow and helping the team identify whether a blockage comes from CI/CD or container execution.

Case technologies

  • Jenkins
  • Docker
  • Kubernetes
  • Linux
Observability · Alerting

HTTP 500 errors with direct Slack notifications

Grafana alerts backed by Prometheus metrics to bring server errors into the team's working channel.

Alerts configured

The challenge
Server errors needed a visible signal for the team without relying on someone watching a dashboard.
What I did
I configured Grafana alerts for HTTP 500 errors using Prometheus metrics and notifications to Slack channels.
What it brings
Errors become operational notifications that the team can investigate from its usual working channel.

Case technologies

  • Grafana
  • Prometheus
  • HTTP
  • Slack
Kubernetes · Automation

Resource auditing by microservice

Scripts to review Kubernetes resources and inform optimization proposals for each microservice.

Audit tooling developed

The challenge
Manual Kubernetes resource reviews made it harder to compare microservices and identify where to investigate adjustments.
What I did
I developed scripts to audit resource usage by microservice and support configuration analysis in Kubernetes environments.
What it brings
A repeatable basis for resource optimization proposals grounded in data from the cluster.

Case technologies

  • Kubernetes
  • Bash
  • Linux
CI/CD · Continuous improvement

Faster delivery at Capgemini

CI/CD improvements that reduced deployment times by more than 80% during my transition toward DevOps.

Documented result

The challenge
The delivery process needed to shorten the time between development changes and deployment.
What I did
I contributed to CI/CD improvements while moving from software development toward automation and DevOps at Capgemini.
What it brings
The improvements reduced deployment times by more than 80%, making application delivery faster.

>80%

Reduction in deployment time

Documented result from my time at Capgemini.

Case technologies

  • CI/CD
  • Automation
Kubernetes · Helm

Validating a Helm chart and its telemetry

Finding an invalid HPA configuration during a dry-run and preparing the chart fix and telemetry integration.

Preparation and dry-run

The challenge
A Helm OCI chart contained an invalid HPA specification that surfaced during pre-deployment validation.
What I did
I found the issue during a dry-run and prepared the chart fix alongside a telemetry integration for local validation.
What it brings
Identifying a configuration problem before deployment and preparing its fix. This case reached preparation and local validation.

Case technologies

  • Helm
  • Kubernetes
  • OCI
  • HPA
Kubernetes · Reliability

Diagnosing memory-related restarts in Kubernetes

Analysis of out-of-memory restarts and proposed requests and limits adjustments based on observed behavior.

Analysis and proposal

The challenge
Containers were restarting due to insufficient memory, requiring an understanding of observed usage relative to configured resources.
What I did
I analyzed restarts by relating requests and limits to application behavior and documented a proposed adjustment.
What it brings
An evidence-based diagnosis to guide memory adjustments. No measured post-change improvement is recorded for this case.

Case technologies

  • Kubernetes
  • Docker
  • Requests / Limits

Tools and practice

Technical skills

Technologies I use, with applied experience distinguished from areas I am still developing.

Strong Applied experience Developing

01 / 07

DevOps & CI/CD

Continuous delivery and deployment standardization.

  • Jenkins / Shared Libraries — Applied experience
  • Git / Bitbucket — Strong
  • GitHub Actions — Applied experience
  • Artifactory — Applied experience

02 / 07

Containers & platform

Containerized applications and cluster operations.

  • Docker — Strong
  • Kubernetes — Applied experience
  • Helm — Applied experience
  • Rancher — Applied experience

03 / 07

Observability

Metrics, logs and production troubleshooting.

  • Prometheus — Applied experience
  • Grafana — Applied experience
  • Graylog — Applied experience

04 / 07

Development

Software engineering background across the delivery cycle.

  • Java / Spring Boot — Applied experience
  • React — Applied experience
  • Node.js — Applied experience
  • TypeScript — Applied experience

05 / 07

Cloud & infrastructure

Growth path toward Platform and Cloud Engineering.

  • Linux — Strong
  • AWS — Developing
  • Terraform — Developing

06 / 07

Automation

Scripts, integrations and reducing manual work.

  • Bash / Shell — Strong
  • Groovy — Applied experience
  • REST APIs / Webhooks — Applied experience
  • Python — Developing

07 / 07

Applied AI & harness engineering

Personal exploration of agents: persistent context, tools and validation.

  • LLMs and tool workflows — Developing
  • MCP tools — Developing
  • Harness engineering — Developing
  • Context design — Developing
  • Hermes Agent — Developing
  • Codex — Developing
  • Gemini — Developing
  • OpenCode — Developing

Continuous learning

Education

2017 — 2021

Bachelor's Degree in Telematics Engineering

Polytechnic University of Cartagena

Courses

Additional training

  • Continuous Delivery & DevOps · University of Minnesota
  • Docker Advanced · Pluralsight
  • Linux Advanced · Pluralsight

Engineering notes

Blog

Notes on software, platforms and what I am learning along the way.

Career path01

From software development to DevOps: seeing the whole system

How a software development background led me toward delivery, operations and automation.

Read note
Personal project02

Building a self-hosted knowledge platform

A look at Outline, containers, data services and custom extensions in a personal project.

Read note
View all posts

Let's talk

Contact

If you’re looking for someone to work on CI/CD, Kubernetes, infrastructure, AI, integration, and automation, I’d love to connect.

CI/CD / Kubernetes / Automation

Let’s make delivery clearer, more automated and more reliable.

Send me an email

contact@javierjimenez.dev

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