Sanaka Lorensuhewa - Full Stack Developer

Overview

Backend Node.js developer (approx Middle level) who builds small REST APIs with authentication and CRUD functionality. The strongest proven skill is implementing JWT-protected user registration and login plus note CRUD, evidenced by controllers/authController.js and controllers/noteController.js. There is limited evidence of schema migration practice, automated tests, performance tuning, or production observability in the public code.

Phone

Technical skills

Languages

9

Python

C++

JavaScript

Node JS

TypeScript

SQL

Dart

C

PHP

Node JS

4

Express

Mongoose

Bcrypt

Dotenv

Mobile

3

Firebase

Flutter

React Native

Databases

3

MySQL

PostgreSQL

Firestore

AI/ML

6

NumPy

Pandas

Scikit-learn

OpenCV

PyTorch

Airflow

Frontend

5

React.js

Tailwind CSS

Chart.js

React Router

Socket.IO

Other

17

Boto3

ROS

CodeIgniter

Figma

GitHub

Rest API

pySpark

Docker

Git

Linux

Prometheus

Machine Learning

PostCSS

CI/CD

MinIO

WebSockets

Computer Vision

Projects

Robust Vision System for Industrial Bin Picking

Jan 2026 to Jan 2026 0 Months

Built a computer vision pipeline to detect and identify deformable and partially occluded objects in cluttered scenes. Used RGB-D input and applied image processing and segmentation for object detection and pick-point estimation. Integrated the vision pipeline with a robotic pick-and-place system.

NEMIS — Near Miss & Hazard Reporting System

Jan 2025 to Jan 2025 0 Months

Developed a near-miss and hazard reporting system for workplace safety workflows. Implemented hazard reporting with photo submission, status tracking, notifications, and review processes. Contributed to both the mobile client and backend API integration.

Local Event Finder (Web & Mobile App)

Feb 2024 to Dec 2024 10 Months

Built a cross-platform event discovery system to help users find local events and improve accessibility. Implemented event categorization, search, and filtering for faster navigation. Added synchronized notifications for real-time updates and improved attendee awareness.

Interactive Tutoring Platform

Feb 2024 to Dec 2024 10 Months

Created a web-based learning platform for admins, tutors, and students. Implemented interactive real-time learning sessions using WebSockets and Socket.io to improve engagement. Worked on scalability and navigation improvements to reduce load times and improve performance.

Personal Finance Tracker

Jan 2024 to Feb 2024 1 Month

Developed an expense tracking application with budget management and basic financial analytics. Implemented interactive charts to visualize spending patterns and trends. Focused on helping users understand spending behavior and make better budgeting decisions.

Timeline

University of Ruhuna (Galle Campus)

Bachelor's Degree · Computer engineering

2021–2026 Galle, Sri Lanka

IT Intern (Mobile and Web Application Developer) · Junior

Sunquick Lanka (Pvt) Ltd · Internship

May 2025 to Nov 2025 6 Months In office

Worked as an IT intern focused on mobile and web application development. Assisted with building and maintaining app features during the internship period. Supported development tasks across the web and mobile codebase, including day-to-day implementation and testing.

Junior Backend Developer Confidence: Medium API Engineer

Backend Node.js developer (approx Middle level) who builds small REST APIs with authentication and CRUD functionality. The strongest proven skill is implementing JWT-protected user registration and login plus note CRUD, evidenced by controllers/authController.js and controllers/noteController.js. There is limited evidence of schema migration practice, automated tests, performance tuning, or production observability in the public code.

API Design

3/10

How well APIs are designed

Clean, conventional REST endpoints with authentication and consistent status codes are implemented, but there is no versioning, pagination, idempotency keys, or documented error contract beyond thrown errors and HTTP statuses.

Evidence

Lorensuhewa/Tech_Note_Hub/backend/controllers/authController.js Lorensuhewa/Tech_Note_Hub/backend/controllers/noteController.js Lorensuhewa/Tech_Note_Hub/backend/server.js

Data Layer & Database

3/10

Working with databases

Uses Mongoose schemas and basic validation (unique email, length checks) and performs query/update operations correctly, but there is no migration history, transaction handling, or explicit handling of N+1 issues or optimized queries.

Evidence

Lorensuhewa/Tech_Note_Hub/backend/models/User.js Lorensuhewa/Tech_Note_Hub/backend/controllers/authController.js Lorensuhewa/Tech_Note_Hub/backend/controllers/noteController.js

Scalability & Performance

1/10

Handling load and speed

Minimal evidence of scalability or performance engineering; standard Express + Mongoose setup is present but no caching, batching, queueing, or explicit connection pooling/optimizations are implemented.

Evidence

Lorensuhewa/Tech_Note_Hub/backend/server.js Lorensuhewa/Tech_Note_Hub/backend/package.json

System Architecture

3/10

Overall system structure

The codebase is modularly structured (routes, controllers, models, middleware), which is appropriate for a small service, but there is no multi-service decomposition, config/secret management beyond dotenv, or documented inter-service contracts.

Evidence

Lorensuhewa/Tech_Note_Hub/backend/routes/noteRoutes.js Lorensuhewa/Tech_Note_Hub/backend/routes/authRoutes.js Lorensuhewa/Tech_Note_Hub/backend/server.js

Security & Auth

4/10

Protecting data and access

Solid basic authentication and credential handling: passwords are salted and hashed, JWTs are verified in middleware, and schema-level validations exist; lacking are refresh/revocation flows, rate limiting, and advanced boundary hardening.

Evidence

Lorensuhewa/Tech_Note_Hub/backend/controllers/authController.js Lorensuhewa/Tech_Note_Hub/backend/middleware/authMiddleware.js Lorensuhewa/Tech_Note_Hub/backend/models/User.js

Reliability & Observability

3/10

Stability and monitoring

There is structured error handling via asyncHandler and an error middleware hook, and sensible HTTP status usage; there is no observable logging, metrics, retry/backoff logic, or graceful shutdown handling shown.

Evidence

Lorensuhewa/Tech_Note_Hub/backend/controllers/authController.js Lorensuhewa/Tech_Note_Hub/backend/middleware/authMiddleware.js Lorensuhewa/Tech_Note_Hub/backend/server.js

Expertise

Node.js· Junior

Microservices & API Architecture· Junior

Technologies

PHP

Node JS· Junior

Rest API

Express

Bcrypt

CodeIgniter

Mongoose

Dotenv

Recommendations

Build and extend small-to-medium REST services that require user authentication and CRUD - for example user-facing APIs or admin microservices.
Hardening and operationalization tasks: add tests, schema migration tooling, structured logging and basic metrics for production readiness.
Work on scaling features like caching, pagination, and request rate limiting, and introduce refresh token / revocation strategies for auth.

Repositories

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

Tech_Note_Hub Smart_Grid Cloud

Intern AI/ML Engineer Confidence: Low Data-centric

Early-career data-centric engineer (intern/junior) who primarily assembles applied data pipelines and basic ML prototypes. The strongest proven skill is building end-to-end data engineering artifacts and orchestration as evidenced by the Smart Grid batch/streaming architecture and orchestration code (airflow dags/daily_billing_dag.py and batch/jobs/billing_reconciliation.py). There is little to no evidence of production-grade custom model training, novel architectures, distributed training optimizations, or independent research in the public code.

Model Architecture & Training

How well models are designed and trained

Not evidenced in public code

Data Pipeline & Feature Engineering

How data is prepared for models

Not evidenced in public code

Experimentation & Evaluation

How results are measured and tested

Not evidenced in public code

MLOps & Deployment

How models are shipped to production

Not evidenced in public code

Computational Efficiency

How efficiently computing resources are used

Not evidenced in public code

Research Depth & Innovation

Depth of research and new ideas

Not evidenced in public code

Expertise

MLOps & Model Lifecycle· Intern

Industries

Artificial Intelligence· Intern

Cybersecurity· Intern

Energy & Utilities· Intern

Technologies

SQL· since 2026 · Junior

C++

MySQL· since 2025

PostgreSQL

Airflow

Firestore

OpenCV· since 2026

MinIO

Prometheus

Scikit-learn

Computer Vision· since 2026

Pandas

NumPy

PyTorch· since 2026

pySpark

Boto3

Machine Learning

Recommendations

Work on batch and streaming ETL pipelines using Airflow and Spark, focusing on observability and data quality enforcement (e.g., implement and own DAGs, metrics, and DQ checks).
Prototype and productionize simple ML models and feature engineering using scikit-learn and Pandas, adding experiment tracking and reproducible notebooks that include data splits and baseline comparisons.
Own infrastructure glue code for cloud object storage and ingestion (boto3 + MinIO/S3 patterns) and add end-to-end deployment tests and runbooks.
Improve code ownership by extracting non-boilerplate, human-authored modules and adding clear unit tests and CI configuration to demonstrate independent engineering.

Repositories

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

Mobile-Phone-Price-Predictor Smart_Grid Cloud

Intern Data Scientist Confidence: Medium Generalist

Generalist software developer at a Junior level specializing in small, well-structured Python command-line applications. Strongest proven skill is modular CLI design and local persistence demonstrated by taskpulse/main.py, taskpulse/manager.py, taskpulse/storage.py and taskpulse/models.py. There is no evidence of data engineering, machine learning, distributed systems, automated tests, or CI in public code.

Statistical Rigor

Correct use of statistics

Not evidenced in public code

Data Wrangling & Cleaning

Preparing and cleaning data

Not evidenced in public code

Exploratory Analysis & Visualization

Exploring and visualizing data

Not evidenced in public code

Predictive Modeling

Building models that predict

Not evidenced in public code

Business Insight & Impact

Turning analysis into business value

Not evidenced in public code

Reproducibility & Notebook Hygiene

Clean, repeatable analysis

Not evidenced in public code

Technologies

Python

Models· mentioned only

Recommendations

Develop small to medium Python CLI tools and internal productivity utilities that need clean separation of concerns.
Implement JSON-backed local persistence utilities and simple UX-driven input validation for command-line apps.
Expand the project with unit tests, type annotations, and packaging to increase maintainability and readiness for production.
Contribute to or prototype features in Python codebases that value modular design and simple storage layers.

Repositories

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

Mobile-Phone-Price-Predictor Smart_Grid TaskPulse_CLI