Adeola Daniel-Oriazowan

Backend • Intern • Management: 3-5 years (6-15 people) • 33 years • Lagos, Nigeria • UTC+01:00 • In office, Partially remote, Fully remote • Work permits: Nigeria • Citizenship: Nigeria • English: C2 • Available in 2 weeks

DevOps Engineer

5+ years exp

Management: 3-5 years (6-15 people)

Python

SQL

Node JS

Java

Bash

Active 26 days ago

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Overview

Technical skills

Timeline

Roles

Overview

A junior API-focused backend developer specializing in operationalizing basic machine learning inference services. The strongest proven skill is building a simple Flask-based inference endpoint that loads a pretrained sklearn model and returns predictions as implemented in app.py. There is little evidence of production-hardened practices such as authentication, input validation, transactional data handling, schema migrations, or scalable deployment configuration.

Phone

Technical skills

Languages

Python

SQL

Node JS

Java

Bash

DevOps

AWS

Azure

Kubernetes

GCP

Amazon EC2

Windows

Amazon EKS

Azure AKS

GitHub Actions

Terraform

Azure DevOps

Ansible

ArgoCD

CloudFormation

DigitalOcean

Akamai

Amazon CloudWatch

OpenTelemetry

GitHub

Google GKE

Grafana

Rest API

Datadog

Docker

Linux

New Relic

IAM

Git

GitLab CI

GitLab

Databases

Apache Kafka

RabbitMQ

Redis

PostgreSQL

MySQL

Other

Express

Bootstrap

JQuery

Postman

Gradle

Prometheus

Rancher

CI/CD

Docker Compose

Helm

Jenkins

Loki

Ubuntu

GitOps

TCP/IP

GDPR

DNS

External Secrets

DHCP

MinIO

Dropbox

SLI/SLO/SLA

Error Budget

Progressive Delivery

Timeline

Lead DevOps Engineer (Platform-Focused) • Lead

CapitalSage Holdings (Ercas) • Full-Time

May 2025 to Present 1 Year 5 Months Lagos In office

Designed and operated platform infrastructure across GCP, AWS, DigitalOcean, and Akamai for fintech workloads supporting 30+ subsidiaries. Owned Kubernetes-based production and non-production platform operations, including deployments, capacity, availability, and operational consistency. Built CI/CD and GitOps delivery workflows with GitHub Actions and Argo CD, improving repeatability and reducing manual deployment effort. Drove production visibility and troubleshooting across Kubernetes, networking, databases, and Redis/RabbitMQ dependencies while applying secure change practices.

Akamai

DigitalOcean

GCP

AWS

Kubernetes

ArgoCD

GitHub Actions

Terraform

Redis

RabbitMQ

IAM

Amazon EKS

Cloud & Infrastructure Engineer • Middle

Seamfix • Full-Time

Apr 2024 to May 2025 1 Year 1 Month Lagos In office

Designed and automated AWS infrastructure using Terraform and Ansible to improve consistency across environments and reduce manual provisioning. Managed CI/CD pipelines for deployments across development and production, supporting reliable application releases. Provided production support and troubleshooting focused on availability and performance. Implemented Redis-based caching to reduce latency and improve application responsiveness.

AWS

Terraform

Ansible

Redis

Lead Cloud Infrastructure Engineer • Lead

Prunedge Development Technology Ltd. • Full-Time

Jul 2021 to Apr 2024 2 Years 9 Months Lagos In office

Managed multi-cloud infrastructure across AWS, Azure, and GCP for enterprise and government systems. Deployed and operated Kubernetes clusters including AWS EKS and Azure AKS for containerized microservices. Built CI/CD and GitOps delivery workflows using GitHub Actions, Argo CD, and Azure DevOps. Implemented Infrastructure as Code with Terraform and Ansible and supported distributed systems involving Kafka and RabbitMQ.

AWS

Azure

GCP

Amazon EKS

Azure AKS

Kubernetes

GitHub Actions

ArgoCD

Azure DevOps

Terraform

Ansible

Apache Kafka

RabbitMQ

Cloud Infrastructure Engineer • Middle

Tech4Dev • Full-Time

Dec 2020 to Aug 2021 8 Months Lagos In office

Provisioned and managed AWS and Azure infrastructure covering compute, networking, and storage services. Automated provisioning using AWS CloudFormation templates and Azure ARM templates, supporting repeatable environment creation. Migrated on-premise Windows applications to AWS EC2 to improve scalability and operational flexibility. Applied DevOps practices including CI/CD, containerization, and monitoring to support ongoing operations.

CloudFormation

Amazon EC2

Azure

Technical Support Engineer • Middle

HAATROB Technical Services • Full-Time

Oct 2017 to Nov 2020 3 Years 1 Month Lagos In office

Provided technical support for infrastructure and application issues across client environments. Supported deployment, maintenance, and troubleshooting for both cloud and on-premise systems. Installed and configured Windows-based IIS web servers and supported Windows infrastructure needs. Assisted with resolving operational problems by coordinating troubleshooting steps and configuration checks.

Windows

University of Ilorin

Bachelor's Degree • Chemistry

2012–2016 Lagos, Nigeria

Intern Backend Developer Confidence: Medium API Engineer

API Design

2/10

How well APIs are designed

Basic HTTP API implemented but missing versioning, idempotency, explicit error contract and input validation.

Evidence

Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: predict route implementation Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: docstring shows expected JSON shape but no validation or schema enforcement

Data Layer & Database

1/10

Working with databases

No database or migration history; lightweight in-memory DataFrame usage and per-request scaling only.

Evidence

Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: scale(payload) uses pandas DataFrame and StandardScaler.fit on the incoming payload Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: model loaded via joblib with no persistence/migration patterns

Scalability & Performance

1/10

Handling load and speed

No caching, no queueing, per-request expensive operations, and the app is run in debug mode without production tuning.

Evidence

Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: scale() calls StandardScaler().fit on each request Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: app.run(..., debug=True) indicating no production-serving configuration

System Architecture

1/10

Overall system structure

Single-file service with no clear decomposition, config management or service contracts.

Evidence

Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: single-file Flask application implementing routes and model load Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: no configuration management or environment-based behavior

Security & Auth

1/10

Protecting data and access

Minimal security posture: no authentication/authorization, no input sanitization, and unsafe model deserialization patterns.

Evidence

Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: predict route accepts JSON with no auth or validation Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: joblib.load used to load model which can be a deserialization risk

Reliability & Observability

2/10

Stability and monitoring

Basic logging is present but there are no resilience patterns, timeouts, retries, structured correlation ids or graceful shutdown handling.

Evidence

Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: create_logger usage and multiple LOG.info calls for input and output Ayanfe5/Operationalize-machine-learning-microservices-API/app.py: absence of try/except around model inference and no shutdown hooks

Expertise

Backend AI & LLM• Intern

Microservices & API Architecture• Intern

Technologies

Rest API

RabbitMQ• since 2021

Apache Kafka• since 2021

Recommendations

Harden inference endpoints by adding input validation (JSON schema), authentication, and clear error contracts for predict requests.
Refactor model serving to avoid per-request expensive operations: pre-fit scalers, load models once, and add request timeouts and graceful shutdown handling for production containers.
Add CI/CD and containerization best practices for deployment and add basic monitoring and health-check endpoints to improve observability and reliability.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

Ayanfe5 portfolio Operationalize-machine-learning-microservices-API

Intern Frontend Developer Confidence: Low Generalist

Junior frontend web developer focused on static portfolio and marketing pages with some basic Node/Express work. The strongest proven skill is crafting and styling static landing pages backed by the Tooplate-based HTML (Ayanfe5/myportfolio/index.html) and a custom stylesheet (Ayanfe5/myportfolio/css/tooplate-ben-resume-style.css). There is little evidence of component architecture, modern frontend tooling, client-side state management, measurable performance work, or production-grade backend/API design in public code.

UI Component Architecture

2/10

How interface parts are built

Monolithic static template with global CSS; no deliberate UI component boundaries, composition, or custom design-system work.

Evidence

Ayanfe5/myportfolio/index.html: single-file template structure (nav, hero, sections) rather than componentized markup Ayanfe5/myportfolio/css/tooplate-ben-resume-style.css: global styles and variables applied across the page instead of modular tokens/components

Responsive & Cross-browser

4/10

Works on all screens and browsers

Responsive layout is handled via media queries and Bootstrap grid classes; basic cross-browser considerations present, but no advanced responsive features or internationalization scaffolding.

Evidence

Ayanfe5/myportfolio/index.html: includes <meta name="viewport"> and Bootstrap grid classes for layout Ayanfe5/myportfolio/css/tooplate-ben-resume-style.css: multiple @media rules adjusting typography and layout at breakpoints

Performance Optimization

1/10

Speed of the interface

No measured performance work, build-time optimizations, or modern performance techniques evident; static assets and no image optimization or code-splitting shown.

Evidence

Ayanfe5/myportfolio/index.html: plain <img> tags used without srcset or picture elements or modern format fallbacks

Accessibility & Semantics

3/10

Usable for everyone

Basic semantic markup and some ARIA attributes exist (inherited from the template); accessibility appears template-driven rather than the result of deliberate accessibility engineering.

Evidence

Ayanfe5/myportfolio/index.html: navbar toggler uses aria-controls and aria-expanded attributes Ayanfe5/myportfolio/index.html: images include alt attributes and form fields have associated <label> elements

State Management & Data Flow

1/10

Managing data in the app

No client-side async state discipline or state machines; forms are HTML-only and backend is a minimal Express endpoint without error handling or race management.

Evidence

Ayanfe5/myportfolio/index.html: contact form uses action="#" method="get" with no client-side validation or async handling Ayanfe5/Jenkins-Nodejs/app.js: express app with a single GET route and no error handling or middleware

UX & Visual Polish

3/10

Look and feel quality

High visual polish, but it is template-driven; presentational quality is good for a landing/portfolio page but UX refinements and production UX patterns are not demonstrated.

Evidence

Ayanfe5/myportfolio/index.html: hero section, testimonials carousel and FAQ markup provide polished layout and interactions Ayanfe5/myportfolio/css/tooplate-ben-resume-style.css: CSS variables and button styling for consistent visual treatment

Expertise

HTML & CSS• Intern

Technologies

Node JS

Express

Bootstrap

JQuery

Recommendations

Build small componentized UI pieces from the template and expose them as reusable components in a framework (React/Next.js or Vue) to demonstrate UI component architecture.
Add basic frontend performance and accessibility work: responsive images (srcset/picture), Lighthouse checks, and ARIA/focus management improvements with before/after metrics.
Expand server work with simple Express routes that include error handling, input validation and unit/integration tests using the existing mocha/supertest setup (Ayanfe5/Jenkins-Nodejs/test/test.js).
Replace copied template parts with original code and document ownership of files to improve originality and demonstrate design intent.

Repositories

The developer's experience in this domain has been verified based on AI analysis of the following repositories:

portfolio myportfolio Jenkins-Nodejs