Master's IT Student
Healthcare Technology Focus

Hi, I'm Peter, a Master's student in Information Technology (graduating April 2026) making a deliberate and passionate career pivot into healthcare technology.

With a professional background in backend development, I'm now focused on solving meaningful, human-centred problems in healthcare.

Driven by deep interest in mental health, I'm building AI-powered mental health platforms while seeking internship opportunities to contribute to healthcare innovation.

Professional Journey

Senior Software Engineer

Majorel | CRM & Business Intelligence

Apr 2022 – Sep 2024

Technical Innovation Focus

Advanced from individual contributor role, focusing on system architecture design and performance optimisation. Contributed to initiatives that improved how our team approached distributed systems and microservices.

35%

Performance Improvement

45%

Scalability Boost

20%

Efficiency Gain

Challenge Spotlight

The GraalVM Migration Project: When our Spring Boot applications were consuming too much memory in production, I initiated the adoption of GraalVM native executables. This involved rewriting critical components to be GraalVM-compatible, resulting in 35% better performance and significantly reduced cloud infrastructure costs.

Software Engineer

Majorel | CRM & Business Intelligence

Jan 2020 – Apr 2022

Foundation Building Years

These were my deep learning years - transitioning from academic knowledge to production systems. Focused on mastering enterprise Java ecosystem and building robust, maintainable code.

Tech Stack Evolution:

Java 8 → Java 17

→

Microservices Architecture

→

Manual Deploy → CI/CD

What I Learned

Systems thinking over feature thinking: The most valuable lesson was learning to see the bigger picture. A 65% deployment time reduction wasn't just about CI/CD - it was about enabling faster feedback loops, reducing developer cognitive load, and building confidence in our release process.

Technical Expertise

Backend Development

Java • Spring Boot • Spring Security • Spring Data JPA • Microservices • MyBatis • GraalVM

AI & Data

Spring AI • Prompt Engineering • RAG Pipelines • Vector Databases • Redis • PostgreSQL

DevOps & Cloud

Docker • Kubernetes • GCP • Jenkins • CI/CD • Nginx • Maven • Git • Terraform (IaC)

Professional Skills

Systems Integration • Cross-team Collaboration • Continuous Learning • Problem-Solving

Personal Projects

Live & Deployed

MindBridge AI

Comprehensive mental health platform designed to make therapeutic practices more accessible and consistent. MindBridge serves as a bridge to an individual's own mind - helping people understand their mental state through AI chat support, mood tracking, and journaling. It's not a replacement for therapy, but reinforcement of therapeutic practices when experts aren't available 24/7.

AI-Powered Mental Health Features:

Intelligent Conversation Routing: Dynamic routing to therapeutic, educational, or general chat modes based on user intent
RAG Pipeline: Personalized AI responses using vector database with user's journals, moods, and mental health knowledge
Content Filtering: Input guardrails ensuring appropriate therapeutic interactions and safety
AI-Generated Insights: Personalized recommendations based on individual mood logs and journal entries

Comprehensive Mental Health Tools:

Interactive Mood Tracking: Mood logging with analytics and trend visualization
Structured Journaling: Full CRUD operations with search, tagging, and mood correlation
Dashboard Analytics: Real-time insights and mental health progress tracking

Enterprise Architecture:

Microservices Architecture: Spring Boot services with Spring Cloud Gateway
Multi-Storage Strategy: PostgreSQL with pgvector, Redis caching, and vector storage
Cloud Deployment: Google Kubernetes Engine with Terraform infrastructure as code
Knowledge Retrieval: Tavily Search API integration for domain-specific mental health knowledge
Secure Authentication: Keycloak OAuth2 with Google/GitHub social login

Spring AI Spring Boot Spring Cloud Gateway PostgreSQL + pgvector Google Vertex AI Keycloak OAuth2 Redis RAG + Vector DB Tavily Search API Docker Google Kubernetes Engine Terraform Nginx Ingress

GitHub Repository Live Demo

Academic Project

Mental Health Predictor

University machine learning project exploring mental health prediction using synthetic lifestyle data. A Flask-based web application that demonstrates predictive modeling concepts with Random Forest classification.

Learning Focus:

Machine Learning Exploration: Hands-on experience comparing Random Forest, Logistic Regression, and SVM models
Data Science Workflow: End-to-end pipeline from data preprocessing to model deployment
Web Application Development: Flask framework integration with scikit-learn for ML model serving
AI Integration: Google Gemini API for generating personalized health recommendations

Technical Implementation:

Model Performance: Random Forest achieved ~78% accuracy on synthetic dataset
Dataset: Synthetic lifestyle and demographic factors for educational purposes
Deployment: Dockerized Flask application with simple web interface
Integration: Now deployed as part of MindBridge platform for demonstration

Python Flask scikit-learn Random Forest Google Gemini API Docker HTML/CSS