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Jerome de Dios

Software Engineering

Software Engineer with over 15 years of experience in development and operations, including Java development, and a proven track record supporting high-traffic sports betting platforms. Skilled in system reliability, troubleshooting, cloud engineering, and collaborating effectively in agile environments. A Masterschool Software Engineering Program graduate, with strong skills in Python, Generative AI, and Cloud Engineering. Committed to building intelligent, scalable solutions and contributing to innovative technology teams, while continuing to develop German language fluency.

Location

Auerbach (Vogtland), Saxony, Germany

Looking for

Full time position

LinkedIn

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Phone Number

Internships

WB

Webeet.io – Amsterdam, North Holland, Netherlands

Intern – Software Development / AI / Cloud

September 2025 – November 2025

Education

MS

Masterschool – Berlin, Germany

Cloud Engineering & Generative AI Engineering

Cloud Engineering

Jun 2025 – Nov 2025

AWSInfrastructure as Code (IaC)Cloud ComputingCloud Storage

Generative AI Engineering

Sep 2024 – Jun 2025

  • Intensive online training in software & AI engineering
  • Hands-on with Python, APIs, JavaScript, Flask, SQL
PythonOpenAI APILangChainLLMsToken OptimizationText EmbeddingsStreams
AD

Asian Development Foundation College, Philippines

Bachelor of Science - BS, Computer Science

Sep 2000 – Jun 2004

Open to Opportunities

Actively seeking Full-Stack / AI Engineer / Java / Application Support roles in Germany and EU

Updated October 2025

Currently Learning

  • A2 German in progress — weekly lessons, online.
  • Rust Programming Language in progress.

Updated October 2025

Projects

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Grocery Web Application

Grocery Web Application

Through this project, I successfully deployed a fully containerized full-stack grocery web application on AWS with Terraform, achieving automated, scalable, and repeatable infrastructure provisioning. Key outcomes included a reliable, production-ready cloud environment with EC2, RDS, S3, and ECS fully orchestrated, secure networking, and JWT-based authentication for users. I learned to design modular Terraform configurations, manage state across environments, and integrate DevOps practices such as CI/CD and Auto Scaling. The project reinforced best practices in cloud security, containerization, and IaC while overcoming challenges such as configuring private/public subnets, handling Terraform dependencies, and debugging ECS deployments. As a result, the deployment process became faster, more consistent, and scalable, reducing manual errors and improving infrastructure reliability. The project is available on GitHub: AWS_grocery, with live deployment and demo steps included in the repo.

Used Terraform for Infrastructure as Code to manage EC2 and RDS resources.Managed scalable EC2 instances with automated provisioning and security via Terraform.”Set up S3 buckets for storage, backups, and Terraform state managementDeployed and maintained RDS PostgreSQL databases with Terraform for secure, optimized performance
Nursing Assistant Application

Nursing Assistant Application

Built and deployed an AI-powered Nursing Assistant application backend using FastAPI, PostgreSQL, and Python, demonstrating practical application of AI integration, modular API design, and secure, scalable cloud-ready architectures. The project addresses challenges in clinical workflows, such as time-consuming handoffs, inconsistent reporting, and information gaps, helping healthcare staff improve efficiency, accuracy, and patient care. Key features include AI-assisted patient handoffs and report generation, JWT/OAuth2-based authentication, Pydantic-driven data validation, modular API endpoints, and structured logging with Loguru/Logfire for monitoring. Implemented relational database integration with SQLModel/PostgreSQL, ensuring persistent, reliable data management. Completed individually as part of the Masterschool AI/Cloud track, the project reinforced best practices in AI integration, secure backend development, and scalable architecture, overcoming challenges such as AI-model orchestration, authentication management, and database consistency. GitHub repo: AI Project Nursing Assistant.

Python (FastAPI): Used to build a modular, high-performance backend API with dependency injection for maintainable and testable code.SQLModel / PostgreSQL: Used for object-relational mapping and persistent data storage to reliably manage patient records and application data.Pydantic: Applied for data validation to ensure all API requests and responses are clean, consistent, and error-free.OAuth2: Implemented authentication to securely control user access and protect sensitive healthcare data.Generative AI & RAG: Integrated AI-powered chat and report generation to assist nurses with patient interactions and documentation.Logfire & Loguru: Configured structured logging and monitoring to track application behavior and ensure operational reliability.
RAG System – Java & Python Microservices

RAG System – Java & Python Microservices

Built two individual backend projects demonstrating expertise in scalable API design and AI integration: a FastAPI Microservice and a Spring Boot AI Chatbot. The FastAPI Microservice showcases a modular architecture with asynchronous endpoints, dependency injection, and RESTful design to solve challenges in building maintainable, testable microservices. It uses FastAPI for high-performance APIs, Docker for containerization, PostgreSQL/SQLModel for persistent storage, and Pydantic for strict data validation—featuring service-based routing, authentication, and clean separation of concerns. The Spring Boot AI Chatbot integrates Spring Boot and Spring AI with OpenAI’s GPT models to enable customizable domain-specific conversations (e.g., bakery, legal, health assistants). It leverages Java 17, PromptTemplate, and Maven to create flexible, structured RESTful APIs with a /chat endpoint for AI responses and modular configurations for easy domain switching. Together, these projects strengthened skills in API orchestration, containerization, AI model integration, and prompt engineering, demonstrating versatility across Python and Java ecosystems

FastAPI: Used to build lightweight, high-performance APIs with asynchronous endpoints and dependency injection for modular, scalable microservices.Docker: Used to containerize the microservices, ensuring consistent environments and simplifying deployment and orchestration.PostgreSQL / SQLModel: Used for reliable, persistent data storage and object-relational mapping to simplify database operations within the microservice architecture.Pydantic: Applied for strict data validation and type enforcement, ensuring clean, predictable API requests and responses.Spring Boot: Used to develop structured RESTful APIs in Java with minimal configuration and strong scalability for the AI chatbot service.Spring AI & OpenAI API: Integrated to enable AI-powered conversational features and prompt-based interactions across multiple business domains.PromptTemplate: Used to manage dynamic prompt construction for flexible and reusable AI responses.Maven: Used for dependency management and project build automation within the Spring Boot chatbot application.
Instagram Clone

Instagram Clone

Used React with TypeScript for building a modular, component-driven UI with type safety. Employed TailwindCSS for responsive, utility-first styling and React Router for seamless navigation and routing. Integrated Vite for fast builds and HMR during development, and Docker for containerized deployment across environments. On the backend, used Fastify for its high-performance Node.js framework, MongoDB for document-based data persistence, and JWT authentication for secure user sessions. Together, these tools support a scalable, maintainable Instagram-like full-stack application.

React (with TypeScript): Used to build a dynamic, component-based frontend with type safety, improving code reliability and maintainability.TailwindCSS: Used for responsive, utility-first styling to create a clean, modern user interface with minimal custom CSS.React Router: Implemented to manage client-side navigation smoothly between pages such as feed, profile, and login.Vite: Used for fast development builds and hot module reloading, accelerating frontend iteration and testing.Docker: Used to containerize both frontend and backend, ensuring consistent builds and seamless deployment across environments.Fastify: Chosen for its high performance and low overhead in handling API requests, ideal for real-time data interactions.JWT (JSON Web Token): Implemented for secure, stateless user authentication across frontend and backend services.

Experience

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Software and Cloud Engineer (Internship)

Sept 2025 - November 2025

Webeet · Remote

  • Built features using TypeScript, Fastify, and React with React Router, following Test-Driven Development (TDD) for reliable, maintainable code.
  • Created secure, scalable, and high-performance solutions with strong data isolation and authentication
  • Automated complex tasks and improved decision-making by integrating AI workflows with n8n and AI APIs.
  • Used modern cloud services and CI/CD pipelines for seamless deployment, monitoring, and ongoing app improvements.
Node.jsReact.jsTypeScriptfastifyReact RouterJestAWSTerraform

Professional Development

Dec 2023 - Present

Masterschool · Remote

  • Completed an intensive Generative AI software engineering program at Masterschool, learning Python, APIs, JavaScript, Flask, and SQL for real-world applications.
  • Gained practical AI Engineering skills through hands-on, industry-focused projects.
  • Building a new life in Germany, balancing family, personal growth, and cultural integration.
  • Learning German to boost professional opportunities and connect with the local community.

Senior Application Support Engineer

Nov 2021 - Nov 2023

Sportserve · Hybrid

  • [Infrastructure & Application Monitoring and Support]
  • Supported critical Sportsbook and Casino apps, ensuring quick updates and issue fixes for users.
  • Monitored system health (apps, servers, networks), addressing alerts and performance issues promptly.
  • Managed monitoring tools, tracked KPIs, and worked with vendors on integrations and issue resolution.
  • [ITSM Processes & Service Management (Internal & Third-Party)]
  • Oversaw end-to-end incident management within defined SLAs — including issue reproduction, triage, troubleshooting, resolution, and post-incident reviews — for internal systems and external customer-facing applications (Sportsbook & Casino).
  • Handled problem management, service/change requests, and participated in release cycles. Contributed to the continuous improvement of the internal knowledge base.
  • [Software Development Lifecycle (SDLC) Support]
  • Performed deployments (frontend/backend), hotfixes, and related tasks in staging and production environments.
  • Worked closely with Network, Platform Engineering, and Development teams to resolve production issues, identify recurring problems, and support ongoing infrastructure needs.
PythonGitDockerTechnical SupportAppDynamicsGrafanaElastic Stack (ELK)PRTGIcingaJiraConfluenceOpsgenieCatchpointJenkinsNexus

Site Reliability Engineer

Sep 2019 - Nov 2021

Bayview Technologies, Inc · On-Site

  • Reduced incident recovery time by 40% through automated monitoring and alerting.
  • Maintained 24/7 sportsbook systems, achieving 99.95% SLA compliance.
  • Ensured 99.99%+ uptime and high QoS for global sportsbook platforms.
  • Improved incident response time by 30% by tracking key SLOs and SLIs.
  • Partnered with sports trading teams to enhance collaboration and reduce silos.
  • Developed self-healing scripts, cutting manual incident work by 50%.
  • Led root cause analyses and post-incident reviews to prevent recurrence.
  • Worked with DevOps and IT teams to ensure smooth releases and production readiness.
  • Followed strict change management, achieving 0% critical outages during deployments.
  • Supported 24/7 operations and participated in on-call rotations.
  • Applied SRE principles to strengthen reliability and operational excellence.
  • Used tools like ELK Stack, Grafana, AppDynamics, and synthetic monitoring to improve performance.
  • Automated deployments with Ansible, Jenkins, and Git, reducing release time by 60%.
  • Used Linux, Bash, Python, and SQL for daily operations and troubleshooting.
  • Communicated effectively with technical and business stakeholders under pressure.
PythonGrafanaJenkinsPRTGShell ScriptingAnsibleAppDynamicsIcinga