Full-Stack & AI Engineer with Cloud & Data Focus
Resume:
BRIAN OSWE FULL STACK SOFTWARE ENGINEER
MACHINE LEARNING ENGINEER
AI ENGINEER
DATA SCIENTIST
CONTACT
***********@*****.***
SKILLS
1.Programming & Frameworks:
Java, Python, JavaScript, SQL
Spring Boot, Hibernate, Django,
Flask, React.js, Next.js, Node.js
RESTful API Development
Tailwind CSS, Bootstrap
2. Software Engineering
System Design and Architecture
Backend Development (Spring
Boot, Hibernate, JPA, Django
Rest Framework)
Frontend Development
(React.js, Next.js, Typescript)
Containerization and
Orchestration (Docker,
Kubernetes)
Cloud Deployment (AWS,
Heroku, DigitalOcean)
3. Data Engineering
ETL Processes, Data
Warehousing
Real-Time Data Streaming
(Kafka, RabbitMQ)
Data Pipeline Development
Big Data Tools: Hadoop,
PySpark, Data Bricks
Cloud Data Solutions: Google
Cloud Platform (BigQuery)
WORK EXPERIENCE
PROFILE
Senior Full Stack & AI Engineer with 5+ years of experience architecting and delivering scalable, data-driven systems. Specializes in bridging the gap between machine learning models and production-ready applications. Proven expertise in designing secure backend services (Spring Boot, Django), building responsive frontends (Next.js, React), and deploying ML models into production environments. Passionate about leveraging cloud technologies (AWS, Docker, Kubernetes) and data engineering tools (Kafka, Spark) to solve complex business problems and drive measurable results. Full Stack Engineer & AI Engineer
RMT Labs SA Luxembourg (Remote)
(May 2025 – Present)
https://rmt-labs.com/
Payment Gateway Architecture: Designed end-to-end microservices payment system integrating Stripe with custom subscription management, implementing webhook-driven event processing for checkout sessions, payment intents, and invoice generation. Architected subscriber-subscription-payment request flow with context- aware plan filtering (personal/company) and automated discount application, ensuring data consistency across internal backend and external payment provider.
AI-Powered Content Integration: Built Azure Service Bus consumer pipeline to retrieve and persist AI-generated company profiles (rich HTML content including About Us, mission, vision, services) from queue messages, enabling automated profile enrichment at scale. Vector Database Implementation: Developed job-candidate matching infrastructure using pgvector extension in PostgreSQL, implementing embedding generation pipeline for job descriptions and user profiles, with plans to support semantic search and intelligent job recommendations.
High-Performance Admin Dashboards: Engineered scalable admin analytics endpoints using Slice-based pagination and JPA interface projections to eliminate costly COUNT queries, reducing database load by avoiding full entity hydration. Implemented month/year filtering across users, companies, and jobs with optimized query patterns for large datasets.
Multi-Channel Notification System: Architected comprehensive notification framework supporting 8+ notification types (job applications, proposals, status updates, security alerts) with actionable events, cross-channel delivery (in-app, email, WhatsApp OTP), and user preference management with granular privacy controls.
4. Java & Spring Boot
Ecosystem
Spring Boot (REST APIs,
Microservices)
Spring Security, Spring
Cloud, Spring Data JPA
Hibernate ORM
Maven / Gradle
JUnit, Mockito for
Testing
API Documentation
(Swagger/OpenAPI)
5. Database Management
Relational Databases:
MySQL, PostgreSQL,
Oracle
NoSQL Databases:
MongoDB, Firebase
6. Version Control &
DevOps
Git, GitHub, Bitbucket,
SVN
CI/CD Tools: Jenkins,
GitLab CI
Monitoring & Logging:
Prometheus, Grafana
7. Machine Learning & AI
Clustering, Decision
Trees, Random Forests,
LSTM, ARIMA
Model Deployment and
Monitoring
Optimization and
Hyperparameter
Tuning
Deep Learning
Frameworks:
TensorFlow, Keras,
PyTorch
Natural Language
Processing (NLP):
Transformers, Hugging
Face
8. Statistical Analysis:
ETL Processes
Data Warehousing
Real-Time Data
Streaming
Data Pipeline
Development
Data Analytics and
Visualization: D3.js
9. Tools
Tableau, Power BI,
JIRA, Trello
Authentication & Security: Implemented phone verification system with WhatsApp OTP integration, rate limiting (3 attempts/5 minutes), token expiration handling, and phone number change workflows. Built admin moderation capabilities including user/company blocking logic and content suspension/republishing.
Subscription Management: Developed company seat allocation system with real-time tracking, automated subscription pause/cancellation on failed payments, payment method and billing address management, and webhook-driven subscription status synchronization.
Frontend Payments & Subscription Experience: Built secure, user- friendly checkout and subscription management flows using Next.js and React, tightly integrated with Stripe APIs and backend webhook- driven workflows. Implemented multi-step checkouts, subscription upgrades/downgrades, proration previews, and real-time validation, with support for 3D Secure (SCA) and resilient error handling across asynchronous payment states.
Technologies: Spring Boot, Java, Python, NextJS, NodeJS, PostgreSQL, pgvector, Azure Service Bus, Stripe API, Azure AI, RESTful APIs, JPA/Hibernate, Redis, Apache Kafka, Docker, CI/CD, Microservices Architecture, Webhook Processing
Full Stack Software Engineer
Proxima AI (February 2021 – April 2025)
https://www.proximaai.co/
Scalable Web Applications: Engineered and maintained scalable web applications using Next.js, Node.js, and Django REST Framework, delivering robust RESTful APIs for real-time analytics, user management, and third- party integrations.
Backend Services & APIs: Built and maintained backend services in Node.js
(Express/Nest-style patterns) alongside Python services, enabling asynchronous processing, efficient I/O handling, and clean service boundaries within a microservices architecture.
Cloud & Serverless Architecture: Optimized cloud deployments on AWS, implementing Lambda-based serverless architectures that reduced infrastructure costs by 20% while improving scalability and fault tolerance. Responsive UI Development: Designed and delivered dynamic, responsive user interfaces with React.js and Tailwind CSS, improving performance, accessibility, and cross-device consistency.
Real-Time Data Pipelines: Implemented real-time data pipelines using Kafka, Redis, and Node.js consumers/producers, powering live dashboards, event- driven workflows, and system monitoring.
CI/CD & DevOps Automation: Established end-to-end CI/CD pipelines with GitHub Actions, automating testing, containerization, and deployments for both Node.js and Python services.
Database Performance & Scalability: Enhanced database performance using PostgreSQL and MongoDB, optimizing queries, indexing strategies, and data models to ensure scalability and data integrity. ML Model Deployment: Collaborated cross-functionally to deploy machine learning models into production, exposing inference through Node.js and Python APIs for real-time predictive analytics.
Product Impact & Reliability: Drove dashboard redesigns that increased user engagement by 25% and reduced system downtime by 30% through improved observability and debugging tooling.
Technologies: Next.js, React, Node.js, Django REST Framework, Python, JavaScript/TypeScript, AWS (Lambda), PostgreSQL, MongoDB, Kafka, Redis, Tailwind CSS, GitHub Actions, Docker, RESTful APIs, CI/CD, Serverless Architecture, Machine Learning Deployment REFERENCE
Mr. Daniel Stoica
CEO, RMT Labs SA
Phone: +352-***-***-***
Email: *******@***-****.***
Mr. Kelyn Ukiru
Lead Software Engineer, RMT Labs SA
Phone: +254*********
Email: **********@*****.***
Mr. Beckham Otieno
CEO Proxima AI
Phone: +254********
Email: *.*******@*****.***
Mr. Manish Kumar
Head of Data Science, Smile Foundation
Phone: +91-901*******
Email: ****@***************.***
IBM Full Stack Software Development
Deep Learning Specialization
deeplearning.ai
Machine Learning Stanford University
Machine Learning Engineering for
Production (MLOps) deeplearning.ai
COURSERA
2019
Business Information
Technology
TECHNICAL UNIVERSITY OF
KENYA
2019-2022
EDUCATION
Technologies Used: Django Rest Framework, Redis, Twilio API Description: Integrated chatbot functionality for client-tenant communication, providing automated responses and personalized assistance.
Impact: Enhanced customer satisfaction and reduced response times by 40%.
3. Real-Time Chatbot Integration System
Technologies Used: React.js, Next.js, Tailwind CSS, Redis, Kafka Description: Developed a dynamic dashboard visualizing call volume, resolution rates, and customer satisfaction in real-time. Impact: Enabled data-driven decision-making, improving call resolution rates by 20%.
2. Call Center Analytics Dashboard
4. Tenant-Based Reporting System
Technologies Used: Django Rest Framework, PostgreSQL, React.js Description: Designed a system to generate tenant-specific reports, including fraud metrics, call analytics, and user feedback. Impact: Improved transparency and empowered clients with actionable insights for their operations.
5. Multi-Tenant SaaS Subscription Platform
Technologies Used: Next.js, Node.js, Django REST Framework, PostgreSQL, Stripe, Redis, Docker
Description: Built a multi-tenant SaaS platform supporting company onboarding, role-based access control, and subscription management. Implemented tenant isolation at the data and API layers, with secure authentication and authorization flows. Features: Subscription plans, seat-based billing, invoice management, webhook-driven subscription state synchronization, and admin dashboards.
Impact: Enabled scalable onboarding of multiple organizations while ensuring billing accuracy and secure tenant separation. https://exklusivautoretrofit.co.ke/
Role: Full Stack Developer
Technologies Used: Next.js, Django Rest Framework, Tailwind CSS, PostgreSQL, WhatsApp API
Features:
Interactive product catalog with advanced filtering and search functionalities.
Integrated a real-time inquiry system with a WhatsApp API, allowing direct customer interaction for service inquiries. Responsive and mobile-friendly design using Tailwind CSS to ensure seamless user experience across devices.
Admin dashboard for managing product listings, images, and service details.
Impact: Increased customer inquiries by 40% through the streamlined inquiry process and enhanced user engagement with the intuitive interface.
1.Eksklusiv Auto Retrofit Website
PROJECTS
Developed a predictive maintenance solution using Python and Pandas for data preprocessing.
Applied machine learning algorithms such as LSTM (Long Short- Term Memory) and Random Forests to analyze sensor data from wind turbines and solar panels.
Integrated with IoT platforms for real-time data streaming and anomaly detection.
Deployed the predictive models using Flask for API integration and Docker for containerization.
The solution reduced maintenance costs by 15% and minimized downtime through proactive maintenance scheduling based on predictive insights.
10. Predictive Maintenance for Renewable Energy Assets Technologies Used: Next.js, React.js, Tailwind CSS, Node.js, PostgreSQL
Description: Built a high-performance admin dashboard for monitoring business KPIs, user activity, and operational metrics. Features: Server-side pagination, dynamic filtering, role-based access, exportable reports, and optimized query patterns for large datasets. Impact: Enabled faster operational decision-making and reduced manual reporting overhead.
9. Admin Analytics & Reporting Dashboard
Implemented a customer lifetime value (CLV) prediction system using Python and Pandas for data preprocessing.
Applied machine learning algorithms such as XGBoost and RandomForest to model customer behavior and predict CLV. Integrated with CRM and transactional databases for data ingestion. Developed a dashboard using Power BI for visualizing CLV insights and segmentation.
The CLV predictions enabled personalized marketing strategies and customer retention initiatives, leading to a 25% increase in customer lifetime value.
8. Customer Lifetime Value Prediction
Technologies Used: React.js, Next.js, Node.js, PostgreSQL, Redis, WebSockets
Description: Developed a real-time customer support system allowing users to create, track, and resolve support tickets with live status updates.
Features: Real-time messaging using WebSockets, ticket prioritization, SLA tracking, role-based dashboards for agents and admins, and audit logs.
Impact: Reduced average response time by 35% and improved customer support transparency.
7. Real-Time Customer Support & Ticketing System
Technologies Used: Node.js, Django REST Framework, Kafka, Redis, PostgreSQL, AWS Lambda
Description: Architected an event-driven notification system supporting email, SMS, WhatsApp, and in-app notifications across multiple products. Features: Message templates, user preference management, retry policies, delivery tracking, and webhook-based event ingestion. Impact: Improved notification delivery reliability and enabled scalable cross-product communication workflows.
6. Event-Driven Notification & Messaging Platform
Developed predictive models using Python, leveraging Pandas and Scikit-learn for data preprocessing and statistical analysis. Applied regression analysis and hypothesis testing techniques to assess program effectiveness and measure social impact. Utilized data visualization tools like Tableau to communicate findings effectively to stakeholders.
The models contributed to evidence-based decision-making and strategic planning, enhancing the effectiveness of targeted interventions within Smile Foundation's initiatives.
15. Sentiment Analysis for Social Media Monitoring Implemented a sentiment analysis system using Python and Natural Language Processing (NLP) techniques.
Utilized NLTK and Scikit-learn for text preprocessing and machine learning model development.
Integrated with Twitter and Facebook APIs for real-time data ingestion. Deployed the sentiment analysis model using Flask and Docker for scalable and efficient processing.
Visualized sentiment trends and insights using Plotly for actionable business insights and decision-making.
14. Predictive Modeling for Program Impact Assessment Implemented customer segmentation analysis using Python, utilizing Pandas for data manipulation and Scikit-learn for clustering techniques such as K-means.
Visualized segmentation results with Matplotlib and Seaborn to identify distinct customer groups based on demographic and behavioral attributes. Collaborated with marketing teams to tailor personalized marketing campaigns, resulting in a 20% increase in customer engagement and conversion rates.
13. Customer Segmentation for Personalization
Developed a credit scoring model using Python and Scikit-learn for machine learning algorithms.
Utilized historical financial data, credit bureau information, and demographic variables for model training.
Applied algorithms such as Logistic Regression and Gradient Boosting Machines (GBM) to assess creditworthiness and predict default probabilities.
Integrated with banking APIs for real-time credit assessment. Deployed the model using Flask for API integration and monitored performance metrics using custom dashboards.
The model improved accuracy in credit decisions and reduced default rates by 12% through data-driven risk assessment.
12. Credit Scoring Model Development
Technologies Used: Next.js, Node.js, Django REST Framework, PostgreSQL, Stripe, AWS S3
Description: Developed a full-featured marketplace connecting service providers with customers, including booking, payments, and reviews.
Features: Provider onboarding, availability scheduling, secure checkout, transaction history, and moderation tools. Impact: Streamlined service discovery and booking workflows, improving user engagement and transaction completion rates. 11. Marketplace Platform for Service Providers
Contact this candidate