SAMUEL.PETER
DevOps & Cloud Engineer

Building resilient, secure infrastructure for critical systems.

DevOps & Cloud Engineer with 19+ years leading high-reliability national grid operations. I design and deliver production-grade CI/CD, cloud-native platforms, and ML-driven systems that improve uptime, security, and operational decision-making.

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19+ years · Critical operations
Cloud & DevOps · Modern delivery
Reliability-first · Secure by design
Portrait of Samuel Peter DevOps & Cloud Engineer
About me

Operational experience.
Engineering-forward delivery.

I am Samuel Peter, a DevOps and Cloud Engineer with more than 19 years of experience as Principal Manager in critical national power-grid infrastructure. My background combines real-time grid operations, incident response, and high-availability systems with modern cloud, automation, and data-driven delivery.

I build solutions that are production-ready and operationally grounded — whether designing contract-first microservices, implementing enterprise CI/CD, or deploying ML recommender systems for asset risk prioritization on AWS. Every project prioritizes reliability, security, maintainability, and measurable outcomes.

Critical infrastructure reliabilityCloud-native & AWSSecurity-first designCI/CD at scale
Education

Academic foundation in DevOps, Cloud & Computing.

Postgraduate study focused on practical DevOps and cloud engineering, supported by a First Class Honours degree in Computing & IT.

Ongoing · Germany

MSc DevOps & Cloud Computing

IU International University of Applied Sciences, Germany

Current GPA: 80.5%

DevOps · 96%Research Methods · 90%Cloud Project · 84%
2020 · United Kingdom

BSc (Hons) Computing & Information Technologies

University of Derby, UK

First Class Honours

2014 · United Kingdom

BTEC Level 5 HND Diploma in Computing and Systems Development (QCF)

Pearson RDI, UK

Selected work

Featured projects

Cloud, DevOps, and secure infrastructure engineering—projects focused on reliability, security, automation, and operationally useful outcomes.

CI/CDPipeline engineering
AWS CloudCloud deployment
Secure infrastructureStandards-led design
ML & microservicesDeployment workflows
Full-stackOperational focus
01 / FEATUREDOPENAPI

Movies API — Microservices Research & Development

A contract-first microservices API built around OpenAPI v3, automated testing, CI/CD, and reproducible performance evaluation.

OpenAPIMicroservicesCI/CDk6
View repository ↗
02 / FEATUREDJENKINS

Credit Loan Calculator — Enterprise CI/CD Pipeline

A full-stack application supported by an enterprise-style pipeline using Jenkins, Maven, Selenium, JUnit, and GitHub Actions.

JenkinsSeleniumGitHub ActionsAutomation
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03 / FEATUREDAWS + ML

HomeStayGrid ML Recommender Feed

A cloud-native recommender workflow using Python, FastAPI, Docker, and AWS to generate maintenance recommendations for grid operations.

FastAPIAWSDockerMachine Learning
View repository ↗
04 / FEATUREDFRONT-END

All Times Fitness Club — Front-End Project

An early front-end project demonstrating progression in layout, UI composition, and responsive web development foundations.

HTMLCSSResponsive design
View repository ↗

Explore the individual project repositories above for implementation details, source code, and evidence of my hands-on experience in DevOps, cloud engineering, and automation.

Notes & reviews

Project insights

Short editorial-style reviews that explain the engineering decisions behind the featured work.

API design · OpenAPI · k6

Why contract-first APIs help teams move with confidence

Starting with an explicit API contract creates a shared agreement between consumers and services. Pairing that contract with automated tests and repeatable load checks makes changes easier to validate. For this project, document the test strategy, service boundaries, and measured performance results.

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CI/CD · Quality gates

From commit to confidence: a layered pipeline

A dependable pipeline makes quality visible: build with Maven, run unit tests, exercise user flows with Selenium, and automate delivery through Jenkins or GitHub Actions. The strongest case study will show pipeline stages, failure handling, and evidence from actual runs.

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AWS · ML · Operations

Turning asset-risk signals into useful maintenance recommendations

ML becomes operationally valuable when its outputs are explainable, timely, and integrated into a team's workflow. Describe the data assumptions, model evaluation, API deployment, container strategy, and safeguards against treating recommendations as unquestionable decisions.

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Contact

Open to new opportunities.

I welcome conversations about DevOps, Cloud Engineering, Platform Engineering, and secure infrastructure roles—particularly in environments that value reliability, automation, and operational excellence.

Email Samuel ↗Download résumé ↓
EMAILsamuelakanimo@hotmail.comGITHUBgithub.com/sampeter-akan ↗LINKEDINlinkedin.com/in/samuel-peter-5117223b ↗
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