01
Frontend & product experience
Responsive applications, accessible interfaces, and dashboards that make complex workflows clear and usable.
- React
- Next.js
- TypeScript
- Tailwind CSS
01 / SOFTWARE ENGINEERING Colombo, Sri Lanka
Full-Stack Developer
& AI/ML Engineer
I build web applications and intelligent systems, connecting thoughtful interfaces, reliable APIs, and machine learning that solves real problems.
// Turning ideas into working software.
export const engineer = {
name: "Mohammed Eajass Yahya",
location: "Colombo, Sri Lanka",
roles: ["Full-Stack Developer",
"AI/ML Engineer"],
frontend: ["React", "Next.js", "TypeScript"],
backend: ["Node.js", "Python", "FastAPI"],
data: ["MongoDB", "MySQL"],
ml: ["scikit-learn", "TensorFlow"],
education: "BSc (Hons) Software Engineering",
focus: "Useful software. Applied AI.",
};
export default engineer; FROM INTERFACE TO INTELLIGENCE
- Academic foundation
- BSc (Hons)Software Engineering
- Selected portfolio
- 04 projectsFull-stack applications & AI
- Applied machine learning
- Models to productsPrediction, evaluation & integration
CORE STACK /ReactNext.jsTypeScriptPythonFastAPIscikit-learn
More about me 02 / PROFESSIONAL PROFILE
SOFTWARE ENGINEERING / APPLIED AITHE ENGINEER
BEHIND THE CODE.
Full-Stack Developer/AI/ML Engineer
Mohammed Eajass Yahya
Colombo, Sri Lanka
I connect the full software stack,
from the interface to the intelligence.
I'm a software engineering graduate building web applications and AI-powered systems. My work brings together frontend development, backend services, databases, and machine learning to solve practical problems.
Across wellness prediction, property valuation, billing, and booking platforms, I focus on clear user experiences, maintainable code, and reliable data. I take models beyond the notebook and connect their outputs to the people using the product.
- Qualification
- BSc (Hons) Software Engineering
- University
- Cardiff Metropolitan University, UKDelivered through ICBT Sri Lanka
- Achievement
- Second Upper DivisionCompleted May 2026
01 / ENGINEERING FOCUS
02
Backend & data architecture
REST APIs, authentication, business logic, and database schemas that keep application data consistent and services maintainable.
- Node.js
- Express
- FastAPI
- MySQL
- MongoDB
03
Machine learning engineering
Data preparation, feature engineering, ensemble learning, and cross-validation for practical prediction problems.
- Python
- scikit-learn
- pandas
- NumPy
04
AI that works inside the product
Model-serving APIs, explainable predictions, automated reports, and analytics that connect trained models to real user workflows.
- Model inference
- FastAPI
- SHAP
- Reporting
02 / EXPERIENCE IN PRACTICEAll 4 projects
FULL-STACK + PREDICTIVE ML
WellSync
Wellness and academic-risk predictions, model-serving APIs, and automated student reports.
View source on GitHubAPPLIED ML + EXPLAINABILITY
Real Estate Price Prediction
Ensemble property valuation, SHAP-based explanations, and a complete management dashboard.
View source on GitHub03 / HOW I WORKPROBLEM TO PRODUCT
- 01
Understand
Clarify the problem, the users, and what a useful outcome looks like.
- 02
Architect
Map the interface, service boundaries, data model, and ML workflow.
- 03
Build & integrate
Connect the application layers and make model outputs usable.
- 04
Test & refine
Validate behaviour, evaluate predictions, and improve through feedback.
03 / TECHNICAL SKILLS
FULL-STACK DEVELOPMENT / APPLIED MLBUILT ACROSS
THE FULL STACK.
From the first component to the model behind it.
I build across the interface, API, and data layers, combining full-stack development with applied machine learning. My toolkit supports the complete path from a working application to an integrated, model-backed feature.
01 / ENGINEERING TOOLKIT06 CAPABILITIES / 39 TECHNOLOGIES
01 / 08 TOOLS
Frontend engineering
Component-driven web applications, responsive layouts, and interactive dashboards that make complex data easy to work with.
- React
- Next.js
- TypeScript
- Tailwind CSS
- Bootstrap
- JavaScript
- HTML5
- CSS3
- Responsive UI
- Reusable components
- Data visualization
02 / 06 TOOLS
Backend & API development
RESTful services, authentication, and business logic for connected applications, from Node.js and Python APIs to Java and PHP systems.
- Node.js
- Express.js
- FastAPI
- Flask
- Java
- PHP
- REST APIs
- JWT authentication
- Servlets & JDBC
03 / 05 TOOLS
AI & machine learning
Structured-data prediction workflows with preprocessing, feature engineering, ensemble models, and evaluation before API integration.
- Python
- scikit-learn
- pandas
- NumPy
- TensorFlow
- Predictive modeling
- Model evaluation
- SHAP explainability
04 / 05 TOOLS
Databases & persistence
Relational schemas and document stores for application records, reporting, and transaction-based billing and booking workflows.
- MongoDB
- MySQL
- PostgreSQL
- MS SQL Server
- SQLite
- Data modeling
- SQL queries
- Transactions & CRUD
05 / 07 TOOLS
Testing & API validation
Unit and component tests, endpoint checks, and API documentation to verify application behavior across JavaScript, Python, and Java.
- Postman
- Swagger
- Jest
- pytest
- Vitest
- React Testing Library
- JUnit
- Unit testing
- Component testing
- OpenAPI documentation
06 / 08 TOOLS
Version control & tooling
Git-based project history, focused development environments, and notebook workflows for implementation, debugging, and ML experiments.
- Git
- GitHub
- VS Code
- PyCharm
- IntelliJ IDEA
- NetBeans
- Jupyter Notebook
- Google Colab
- Git workflows
- Debugging
- Notebook experiments
02 / APPLIED MACHINE LEARNINGDATA TO PRODUCT
Beyond the notebook.
My wellness and property-valuation projects connect trained models to prediction services, explanations, and user-facing dashboards.
01Prepare
Clean and structure the dataset.
02Engineer
Transform and select useful features.
03Evaluate
Train, validate, and compare models.
04Serve
Expose predictions through an API.
05Integrate
Connect insights to the interface.
04 / SELECTED PROJECTS
FULL-STACK ENGINEERING / APPLIED AISELECTED WORK.
REAL SYSTEMS.
Interfaces. Services. Data. Intelligence.
Four projects spanning predictive analytics, business operations, and booking platforms. Each brings together the application layers and engineering decisions behind a complete user workflow.
PROJECT INDEX04 PROJECTS / 02 AI/ML SYSTEMS
01Predictive wellness platform
AI / MLStudent wellness & academic analytics
WellSync
Full-stack development & ML integration
A student wellness platform that brings lifestyle inputs, predictive models, and role-based dashboards into one application. Students can review their results while administrators work with reports and prediction history.
The challenge
Connect student lifestyle data to understandable wellness insights and a consistent reporting workflow.
- prediction workflows
- 03
- automated reports
- Weekly
- student & admin access
- Role-based
- Next.js
- TypeScript
- Node.js
- Express.js
- FastAPI
- Python
- MongoDB
- scikit-learn
- INTERFACE
Next.js + TypeScript
Student and administrator dashboards
- APPLICATION
Node.js + Express
Application APIs and reporting workflows
- INTELLIGENCE
Python + FastAPI
Model inference and prediction services
- PERSISTENCE
MongoDB
Lifestyle records and prediction history
Wellness, stress, and academic-impact predictions connected to useful reporting.
Engineering details06 IMPLEMENTATION POINTS
Developed separate workflows for wellness scoring, stress categorization, and academic-impact prediction.
Separated model inference into a FastAPI service connected to the application API.
Created scheduled wellness reports, recommendations, email templates, and PDF-ready reporting.
Built student and administrator views for analytics, notifications, prediction history, and exports.
Evaluated model behavior with cross-validation and prediction-specific metrics.
Tested frontend, backend, and inference workflows with Jest, React Testing Library, and pytest.
02Applied ML & explainability
AI / MLExplainable property valuation
Real Estate Price Prediction
Full-stack development & ML engineering
A property-valuation application connecting ensemble machine learning to a React interface and management dashboard. The system supports individual and batch predictions, with explanations that make model outputs easier to interpret.
The challenge
Turn property attributes into useful price estimates, with explanations and a manageable prediction history.
- valuation pipeline
- Ensemble
- prediction explanations
- SHAP
- property processing
- Batch
- React
- Node.js
- Python
- MongoDB
- XGBoost
- LightGBM
- CatBoost
- SHAP
- INTERFACE
React
Valuation forms and analytics dashboards
- APPLICATION
Node.js
Role-based access and management APIs
- INTELLIGENCE
Python
Ensemble predictions and SHAP explanations
- PERSISTENCE
MongoDB
Properties, users, and prediction records
Property estimates, model explanations, and administration in one workflow.
Engineering details06 IMPLEMENTATION POINTS
Combined XGBoost, LightGBM, and CatBoost in an ensemble valuation workflow.
Used feature-importance analysis and SHAP to explain the factors behind predictions.
Supported batch processing for multiple property valuations in a single workflow.
Implemented JWT authentication with separate user and administrator access.
Created dashboards for prediction history, usage statistics, and valuation results.
Added management workflows for users, properties, prediction records, and quotas.
03Java business application
Full-stackBilling, inventory & transaction reporting
Pahanaedu Bill Management
Java full-stack development
A Java-based business application for customer management, inventory, and billing. A relational data model connects bills to customers and items, while dashboards summarize transactions and revenue.
The challenge
Keep customer records, item pricing, billing, and reporting consistent across daily business operations.
- customer, item & bill records
- CRUD
- discount handling
- Item-level
- revenue reporting
- Chart.js
- Java
- JSP
- Servlets
- JDBC
- MySQL
- Bootstrap
- Chart.js
- INTERFACE
JSP + Bootstrap
Billing screens and business dashboards
- APPLICATION
Java Servlets
Request handling and business operations
- DATA ACCESS
JDBC
Structured queries and database operations
- PERSISTENCE
MySQL
Customers, items, bills, and bill line items
Connected billing records, inventory workflows, and operational reporting.
Engineering details06 IMPLEMENTATION POINTS
Implemented customer, item, and billing CRUD operations with Java Servlets and JDBC.
Designed relational tables and bill-item relationships to keep transaction records connected.
Added percentage-based item discounts to support billing and pricing workflows.
Built Chart.js dashboards for revenue trends, transaction summaries, and bill status.
Supported bulk bill-status changes to reduce repetitive administration.
Recorded timestamps and structured transaction data for audit-focused reporting.
04Multi-role booking platform
Full-stackCourt availability, booking & payment status
Futsal Reservation
Full-stack web development
A booking platform connecting customers, venue owners, and administrators. Date-based availability, transaction-backed reservations, and payment-status tracking bring the booking lifecycle into one system.
The challenge
Coordinate court availability and reservations while preventing conflicting bookings.
- user roles
- 03
- court availability
- Hourly
- booking transactions
- Atomic
- HTML
- CSS
- JavaScript
- PHP
- MySQL
- INTERFACE
HTML + CSS + JavaScript
Court discovery and reservation screens
- APPLICATION
PHP
Authentication and role-specific workflows
- BOOKING
PHP + SQL transactions
Slot confirmation and payment-status updates
- PERSISTENCE
MySQL
Courts, slots, reservations, and payments
A coordinated path from available court slots to confirmed reservations.
Engineering details06 IMPLEMENTATION POINTS
Built separate management workflows for administrators, venue owners, and customers.
Generated hourly court slots across configured date ranges.
Added date-based availability fetching and prevented already-booked slots from being reused.
Used database transactions to commit booking records and slot changes together, or roll both back.
Tracked pending and completed payment states using generated booking payment identifiers.
Designed a relational schema for users, venues, courts, slots, reviews, and payments.
05 / EDUCATION
SOFTWARE ENGINEERING / ACADEMIC FOUNDATIONTHE FOUNDATION.
BEHIND THE BUILD.
From computing fundamentals to applied engineering.
My academic path combines a computing and software engineering foundation with honours-level study. I carry that learning into full-stack applications, database-backed workflows, and AI/ML features that connect models to real users.
01 / ACADEMIC RECORD02 COMPLETED QUALIFICATIONS
01 / Honours Degree
Completed
BSc (Hons) Software Engineering
- Classification
- Second Upper Division
- Institution
- Cardiff Metropolitan University, UK
- Delivery partner
- ICBT Sri Lanka
An honours-level foundation in software engineering, applied through full-stack applications, data-driven systems, and machine learning integration. This stage of my education connects system design with practical implementation and evaluation.
Engineering foundation
- Full-stack architecture
- Backend API services
- Data modeling
- Predictive analytics
- Feature engineering
- Model evaluation
How I apply it
- Translated requirements into interfaces, service boundaries, and structured data models.
- Applied machine learning through data preparation, model evaluation, and prediction-serving APIs.
- Connected implementation, testing, and documentation across complete software projects.
02 / Higher Diploma
Completed
Higher Diploma in Computing and Software Engineering
- Classification
- Merit
- Institution
- Cardiff Metropolitan University, UK
- Delivery partner
- ICBT Sri Lanka
The computing and software engineering foundation behind my degree pathway: structured programming, web development, relational data, and system analysis. It established the problem-solving habits I carry into full-stack development.
Engineering foundation
- Programming fundamentals
- Object-oriented development
- Web application development
- Relational databases & SQL
- System analysis & design
- Testing & debugging
How I apply it
- Built a foundation for connecting frontend screens, backend logic, and database operations.
- Practiced structured problem-solving through implementation, validation, and debugging.
- Developed documentation and project presentation skills alongside the pathway to honours-level study.
02 / LEARNING IN PRACTICEPortfolio projects
Knowledge, put to work.
Academic foundations become practical skills through building, testing, and refining complete applications.
- 01
Full-stack development
Connecting responsive interfaces, application APIs, and persistent data into one usable system.
- Next.js
- Node.js
- MongoDB
- 02
Applied AI & machine learning
Turning prepared data and evaluated models into predictions, explanations, and user-facing insights.
- Python
- React
- MongoDB
- 03
Data & business systems
Using relational schemas, business rules, and reporting to keep application records consistent.
- MySQL
06 / SOFT SKILLS
PEOPLE / PROCESS / ENGINEERINGHOW I WORK.
WHAT I BRING.
Clear thinking. Shared context. Reliable follow-through.
My approach to full-stack development and AI/ML engineering goes beyond implementation. I bring structured problem-solving, thoughtful communication, and a practical approach to collaboration, learning, and delivery.
01 / PROFESSIONAL PRACTICES06 WORKING STRENGTHS
01
Understand before changing
Analytical problem-solving
I break complex problems into smaller questions, test assumptions, and use evidence to identify the cause before choosing a solution.
- In practice
- Trace an issue across the interface, API, database, or prediction pipeline, then verify the fix against the original failure.
Working habits
- Root-cause analysis
- Testable assumptions
- Solution validation
02
Keep the context shared
Collaborative development
I make my work understandable to others through focused changes, useful progress updates, and constructive responses to review.
- In practice
- Align on API contracts, data formats, and feature boundaries so frontend, backend, and model integration work can connect cleanly.
Working habits
- Shared expectations
- Reviewable changes
- Early blocker updates
03
Make complexity understandable
Clear technical communication
I explain technical choices in terms of the problem, the trade-offs, and the user impact, while making room for questions and feedback.
- In practice
- Turn requirements into acceptance criteria and explain model outputs, limitations, and uncertainty without overstating what AI can do.
Working habits
- Requirement clarity
- Documented decisions
- Honest ML explanations
04
Learn with a purpose
Adaptability & learning
I approach unfamiliar tools and changing requirements with small experiments, deliberate practice, and a willingness to revise my approach.
- In practice
- Compare an implementation or model against a simple baseline, learn from the result, and adopt changes that address the actual need.
Working habits
- Focused experimentation
- Feedback-led improvement
- Practical tool choices
05
Keep discussions productive
Constructive decision-making
I listen to different perspectives, separate preferences from constraints, and look for a practical decision the work can move forward with.
- In practice
- Discuss architecture or model choices using maintainability, latency, data quality, and delivery scope as shared decision criteria.
Working habits
- Active listening
- Evidence-led discussion
- Respectful disagreement
06
Follow through on the details
Ownership & prioritization
I organize work into manageable milestones, raise risks early, and treat testing and documentation as part of delivering a complete feature.
- In practice
- Prioritize an end-to-end working path, make progress visible, and leave time to validate both application behavior and model integration.
Working habits
- Realistic scope
- Visible progress
- Reliable follow-through
02 / COLLABORATION IN PRACTICESee the work
From shared understanding
to a working result.
A simple rhythm for approaching a feature, an application, or an ML integration: make the goal clear, keep the work visible, and leave room for feedback.
01Clarify
Understand the user, the problem, and what a useful result looks like.
Scope & success criteria02Align
Surface constraints, agree on priorities, and break the work into clear steps.
Tasks & trade-offs03Build
Work in focused increments, share progress, and bring blockers into view early.
Reviewable changes04Review
Check behavior, discuss feedback, and document the decisions and remaining limits.
Validation & handover
07 / PROFESSIONAL FIT
OPEN TO ENGINEERING OPPORTUNITIESFULL-STACK DEVELOPER.AI/ML ENGINEER.
Connecting applications, data, and machine learning.
I bring a software engineering foundation and hands-on portfolio work across web applications, prediction services, and database-backed systems. I'm looking to contribute to teams building useful products with thoughtful engineering.
01 / WHERE I CAN CONTRIBUTEExplore all projects
FULL-STACK DEVELOPMENT
Connected web applications
A fit for work that needs the interface, application services, and data layer to function together as a complete user experience.
- Responsive dashboards and role-based application workflows.
- Backend APIs, authentication, and persistent application records.
- Validation and testing across the complete user journey.
Portfolio evidence
WellSyncWellness, stress, and academic-impact predictions connected to useful reporting.
- Next.js
- Node.js
- MongoDB
APPLIED AI / ML ENGINEERING
ML-powered product features
A fit for turning structured data and predictive models into useful application features, with evaluation and clear explanations of model behavior.
- Data preparation, feature engineering, and predictive modeling.
- Model comparison, evaluation, and interpretable results.
- Prediction services connected to dashboards and user workflows.
Portfolio evidence
Real Estate Price PredictionProperty estimates, model explanations, and administration in one workflow.
- Python
- React
- MongoDB
02 / LET'S DISCUSS THE FITROLE / PROJECT / COLLABORATION
Let's talk about what you're building.
Share the problem, the role, and what your team needs to build. Let's discuss where my full-stack and AI/ML experience can contribute.
Colombo, Sri Lanka
08 / CONTACT
FULL-STACK DEVELOPMENT / AI & MLLET'S START
A CONVERSATION.
An engineering opportunity. A product idea. A useful collaboration.
I'm open to full-stack developer and AI/ML engineering opportunities, project collaborations, and technical conversations about building useful software. Let's connect around the problem you want to solve.
01 / GET IN TOUCHOPEN TO OPPORTUNITIES
yahyame2005@gmail.comCopy email address
Engineering opportunities, project briefs, and collaboration.
Phone(+94) 77 451 4971Direct professional contactCopy phone number
Let's discuss
- Full-stack roles
- Applied AI & ML
- Product collaboration
Mohammed Eajass Yahya
Full-Stack Developer / AI/ML Engineer
I build connected applications and model-backed features, with a focus on clear interfaces, maintainable services, and practical results. I value thoughtful technical work and straightforward communication.
- Based in
- Colombo, Sri Lanka
- Collaboration
- Remote-friendly