Christsanctus Chinedu
Full Stack Developer
4+ years exp
MATLAB
JavaScript
Node JS
TypeScript
Python
Active 3 days ago
+34 (90) 52612288
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Overview
Technical skills
Timeline
Roles
Overview
Full-stack web developer at a middle level focused on Next.js/React applications and backend integrations. Proven strength in building end-to-end client-server features and integrations as evidenced by Next API routes (pages/api/post/[id].ts), client signup/signin flows (Utils/index.ts), and a custom token generator (Wapme_sever/utils/TokenGenerator.js). There is little public evidence of automated tests, formal accessibility engineering, or measured performance optimizations.
Technical skills
MATLAB
JavaScript
Node JS• Middle
TypeScript• Middle • 4y+
Python• Middle • 3y+
MATLAB
Simulink
Node JS
Axios
Express
Prisma
Multer
AI/ML
OpenCV
PyTorch
Google Colab• 3y+
YOLO• 3y+
ResNet
TensorFlow
Computer Vision
Frontend
Next.js
React.js
Tailwind CSS
Zustand
SWC
DevOps
Git
WebRTC
Docker• 3y+
Design
SolidWorks
AutoCAD
Robotics
Motion Planning
Perception
SLAM
ROS
Timeline
University of Nigeria
Bachelor's Degree •
Mechatronic Engineering
Full Stack Developer
•
Middle
Airweb Software Solutions
•
Full-Time
Developed and deployed a hotel management application used across multiple hotel locations. Built staff dashboards to manage hotel operations, users, and daily activity workflows. Implemented the solution as a full-stack system with backend APIs and database integration.
Research Engineer
•
Middle
University of Nigeria
•
Full-Time
Created a web application for remote monitoring, configuration, and communication with an intelligent bird deterrence system. Developed and assessed computer vision models for real-time bird detection and classification, using deep learning architectures. Extended the funded research work into a complete final-year engineering solution.
YOLO
ResNet
TensorFlow
Research Assistant (Tutor)
•
Middle
University of Nigeria
•
Full-Time
Built a computer vision system for autonomous cassava harvesting and navigation while supporting academic supervision. Designed and created a prototype for agricultural field image acquisition. Collected and annotated agricultural imagery, trained and evaluated YOLO models using Google Colab, and provided technical mentoring in Python and applied AI development.
YOLOsince 2023
Google Colab
Machine Vision Tutor (Computer Vision)
•
Middle
University of Nigeria
•
Full-Time
Delivered paid practical machine vision training to final-year mechatronics engineering students and supported lab activities. Assisted with instruction and assessment preparation. Contributed to related control and robotics research activities during the teaching period.
Research Assistant (Artificial Intelligence & Computer Vision)
•
Middle
University of Nigeria
•
Full-Time
Collected and annotated data and trained an object detection model for selective fertilizer spraying using a quadcopter. Used embedded systems, containerized the model, and deployed it onto an NVIDIA Jetson Orin GPU. Supported lab instruction, assessments, and daily research operations.
Docker
Middle Backend Developer
Confidence: Medium API Engineer
Full-stack developer at an early-career to middle level specializing in Node.js APIs and real-time web and media applications. The strongest proven skill is implementing low-level token and encryption logic as shown in Wapme-whatsapp-clone/Wapme_sever/utils/TokenGenerator.js. There is limited evidence of production-grade architecture, migration-driven database work, comprehensive error handling, observability, or robust operational patterns in server code.
API Design
3/10
How well APIs are designed
Basic REST/Next API routes exist with GET/PUT handlers but there is no versioning, no global error contract, and idempotency/validation patterns are minimal.
Evidence
vshare-VideoSharing/pages/api/post/[id].ts
vshare-VideoSharing/pages/api/profile/[id].ts
Wapme-whatsapp-clone/Wapme_sever/index.js
Data Layer & Database
3/10
Working with databases
Data access is implemented via Sanity GROQ queries and client.fetch with some patch/commit usage but there is no migration history, transaction handling, or explicit protection against query interpolation risks.
Evidence
vshare-VideoSharing/Utils/querries.ts
vshare-VideoSharing/pages/api/post/[id].ts
Scalability & Performance
2/10
Handling load and speed
Little evidence of scalability work such as caching, queues, connection pooling, or rate limiting; TokenGenerator shows performant binary handling but overall scaling patterns are absent.
Evidence
Wapme-whatsapp-clone/Wapme_sever/utils/TokenGenerator.js
System Architecture
3/10
Overall system structure
Projects are separated into client and server folders showing basic modular boundaries for web and API layers, but there is no service decomposition rationale, config management, or graceful-degradation patterns.
Evidence
Wapme-whatsapp-clone/Wapme_client
Wapme-whatsapp-clone/Wapme_sever
vshare-VideoSharing/pages/api
Security & Auth
3/10
Protecting data and access
Some explicit security work exists (custom AES token creation with validation) but there are clear issues such as leaked Firebase config, interpolated GROQ queries and missing middleware invocation that reduce overall security posture.
Evidence
Wapme-whatsapp-clone/Wapme_sever/utils/TokenGenerator.js
Wapme-whatsapp-clone/Wapme_client/src/utils/FirebaseConfig.js
vshare-VideoSharing/Utils/querries.ts
Reliability & Observability
2/10
Stability and monitoring
Minimal reliability and observability patterns; no retries/backoff, structured logging, graceful shutdown, or metrics are present in server code.
Evidence
Wapme-whatsapp-clone/Wapme_sever/index.js
vshare-VideoSharing/pages/api/post/[id].ts
Expertise
Node.js• Middle
Messaging & Real-time• Middle
Microservices & API Architecture• Middle
Industries
Internet Services• Middle
Media & Entertainment• Middle
Technologies
Node JS• Middle
Express
WebRTC
Multer
Axios
Recommendations
- Build and extend realtime chat or media features and backend APIs where custom token/auth handling and WebSocket integration are required.
- Harden and productionize existing services: add input validation, structured logging/correlation ids, graceful shutdown, and standard retry/backoff patterns.
- Work on data-layer maturity: introduce migrations/versioning, transactional boundaries, and safe query parameterization instead of direct string interpolation.
- Implement basic observability and SRE practices for services: metrics, alerts, and request tracing for critical endpoints.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Frontend Developer
Confidence: Medium Fullstack
Full-stack web developer at a middle level focused on Next.js/React applications and backend integrations. Proven strength in building end-to-end client-server features and integrations as evidenced by Next API routes (pages/api/post/[id].ts), client signup/signin flows (Utils/index.ts), and a custom token generator (Wapme_sever/utils/TokenGenerator.js). There is little public evidence of automated tests, formal accessibility engineering, or measured performance optimizations.
UI Component Architecture
2/10
How interface parts are built
Minimal original component architecture visible; styling and token decisions exist but there is little evidence of custom component composition, design-system work, or non-trivial custom hooks in the human-authored files.
Responsive & Cross-browser
3/10
Works on all screens and browsers
Responsive work is based on Tailwind and custom breakpoints and extended sizes; no advanced responsive techniques (container queries, RTL/i18n readiness) are present in the analyzed human-authored files.
Performance Optimization
2/10
Speed of the interface
Some build/runtime optimizations are enabled (Next config, image domains, swcMinify) but there is no measured performance work, bundle analysis, list virtualization or targeted rendering optimizations in the human-authored code.
Accessibility & Semantics
1/10
Usable for everyone
Little explicit accessibility work is visible; no aria/focus management, a11y tests or CI axe/linter configs were present in the reviewed human-authored files.
State Management & Data Flow
4/10
Managing data in the app
Clear client-server data flows and simple server-state updates are implemented; there are API routes, Sanity client queries, axios-based signup/signin flows and a custom server-side token generator showing understanding of state, network flows and backend integration, but advanced server-state discipline (cancellation, optimistic rollback, complex state machines) is not present.
UX & Visual Polish
3/10
Look and feel quality
Visual polish and UX touches are present via custom Tailwind theme tokens, background assets and scrollbar styling, but UX practices like skeletons, undo patterns, or comprehensive edge-state UI are not visible in the analyzed files.
Expertise
React• Middle
Frontend Architecture & Build Tools• Middle
Industries
Internet Services• Middle
Media & Entertainment• Middle
Technologies
JavaScript
TypeScript• Middle • 4y+
Zustand
SolidWorks
Tailwind CSS
Next.js
Prisma
React.js
AutoCAD
SWC
Recommendations
- Build end-to-end social or realtime features (Next.js frontend + API routes + Sanity or Prisma backend) including tests and explicit edge-state handling.
- Focus on adding automated tests and CI (unit and integration) around auth, API routes and data flows to raise reliability.
- Improve accessibility and keyboard/focus management on custom widgets and add a11y linting into CI.
- Add measured performance work: bundle analysis, code-splitting strategies and before/after metrics for major optimizations.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle DevOps Engineer
Confidence: Medium Generalist
Edge computer vision developer (Middle) focused on small-scale image classification and data-collection tooling with a practical hands-on approach. The strongest proven skill is implementing lightweight image inference and preprocessing pipelines as demonstrated by the image processing and TFLite inference logic in collection/birdcam/inference.py. There is little to no evidence of production practices such as CI/CD, containerization, infrastructure-as-code, observability, or structured testing in the public human-authored code.
CI/CD Pipelines
Automated build and deploy
Not evidenced in public code
Infrastructure as Code
Managing servers with code
Not evidenced in public code
Containerization & Orchestration
Working with containers
Not evidenced in public code
Observability & Monitoring
Watching system health
Not evidenced in public code
Reliability & Incident Response
Keeping systems up
Not evidenced in public code
Cloud & Cost Optimization
Smart use of the cloud
Not evidenced in public code
Industries
Artificial Intelligence• Middle
Technologies
Python• Middle • 3y+
Recommendations
- Build and harden end-to-end deployment pipelines for the model code, starting with a simple CI that runs linting and unit tests and then a CD step to push a container or artifact to a registry.
- Add error handling and input validation around file and camera operations, and remove hardcoded paths by parameterizing configuration with a single config file or environment variables.
- Introduce lightweight reproducible packaging such as a Dockerfile and simple integration tests to validate camera input, inference, and file movement under controlled conditions.
- Add observability: structured logging, basic metrics (inference latency, success/failure counts), and a simple alerting rule to detect inference failures or camera errors.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: