Luanderson Mendes

Development of end-to-end software and Artificial Intelligence solutions focused on real business problems, from the user interface to data orchestration and AI agents, delivering durable software without unnecessary complexity.

• AI engineering and agents: building and orchestrating AI agents in Python for decision support and replacing repetitive manual work.
• LLM integration: language models via OpenRouter and local model execution via Ollama, applying technical judgment to model selection and usage.
• Frontend: clear, responsive, and efficient web interfaces in React and TypeScript, focused on usability and reducing operational friction for users.
• Backend and data: modeling resilient architectures and reliable relational data structures in PostgreSQL.
• DevOps: containerization and standardization of development and production environments with Docker.
• Close daily collaboration with distributed frontend, product, and DevOps stakeholders; use of Claude Code and Codex to accelerate delivery while keeping code clean, modular, and well documented.

Development and maintenance of backend solutions, systems integration, and application evolution, with a focus on quality and software engineering best practices.

End-to-end development for clients and freelance projects, turning business needs into scalable, high-impact solutions.

• Architecture and backend: RESTful APIs and microservices in Java (Spring Boot) and C# (ASP.NET Core), applying Clean Architecture, SOLID, and DDD to distributed systems.
• Data and security: high-performance modeling in PostgreSQL, MySQL, and MongoDB with JPA/Hibernate and EF Core; access control with Spring Security, ASP.NET Identity, and JWT.
• DevOps and observability: Docker containerization, CI/CD pipelines, structured logging, and continuous production monitoring.
• Quality: automated tests with JUnit/Mockito (Java) and xUnit/Moq (C#), focused on preventing regressions.
• Full stack and integrations: React and TypeScript interfaces; complex integrations through external APIs and webhooks.
• Product engineering: requirements gathering with stakeholders, expectation management, and version control (Git/GitHub) from planning to deployment.

Software Engineering, DevOps, and Infrastructure: automation, observability, systems integration, and support of corporate environments.

• Design and evolution of RESTful APIs in Java (Spring Boot) and C# (.NET), including authentication and authorization flows secured with Spring Security (JWT); optimizations resulting in a 30% increase in scalability and a 20% increase in overall system performance.
• High-performance, low-latency data modeling combining PostgreSQL, SQL Server, MongoDB, and Redis for distributed architectures, including administration, advanced queries, and performance analysis.
• Resilient integrations between internal and third-party systems; AI-powered automations and chatbots with Python and n8n to streamline support and operations processes.
• Full observability stack (Prometheus, Grafana, Loki, Zabbix) with dashboards, metrics, and structured logging: sustained 99.9% SLA and a 60% reduction in Mean Time to Resolution (MTTR).
• Management of Microsoft Azure environments (VMs, Storage, Networking) and orchestration of containerized microservices with Docker and Kubernetes, driving a 40% gain in operational efficiency.
• CI/CD pipelines with Azure DevOps, GitHub Actions, and Jenkins for continuous deployments and fast hotfixes; TDD with high test coverage (JUnit, Mockito), code reviews, GitFlow, and agile rituals (Scrum/Kanban).
• Support of Windows and Linux environments, monitoring of critical services, and advanced troubleshooting.

• Diagnosis and resolution of connectivity incidents in residential and corporate environments, focused on network stability.
• Configuration and support of network equipment (routers, switches, access devices).
• Direct customer-facing technical support, building a solid foundation in communication and problem solving.

• AI-powered full stack application that simulates technical recruiter evaluations and returns automated feedback to candidates; designed, built, and shipped independently end-to-end, from architecture to deployment.
• Traction and reliability: over 1,000 unique users and 1,000 analyses processed in the first week, with 100% uptime in production.
• Microservices backend with Java 25 and Spring Boot 3.5; prompt orchestration and natural language processing with OpenAI via LangChain4j.
• Frontend in Next.js 16, React 19, and TypeScript; PostgreSQL persistence and scalable caching with Redis; automated cloud deployment via CI/CD and Docker.
• High automated test coverage, with rigorous application of Clean Code, SOLID, and Design Patterns.

• Full stack, production-ready, containerized reference application demonstrating secure JWT authentication and Clean Architecture.
• HttpOnly/SameSite cookie-based JWT with short-lived access tokens and silent refresh, avoiding XSS-prone localStorage token storage; passwords hashed with Argon2id plus a server-side pepper.
• C#/.NET backend with Entity Framework Core in strict layers (Domain, Application, Infrastructure, API), following SOLID and Dependency Inversion; automated retry logic for resilient database startup in Docker.
• Next.js (App Router) frontend using Server Components to read HttpOnly cookies server-side; full unit test suite (xUnit, Moq, FluentAssertions) covering token generation and password hashing; Docker Compose.

AI Engineering and LLMs · Generative AI · RAG (Retrieval-Augmented Generation) · Autonomous Agents and Agentic AI · Model Context Protocol (MCP) · AI System Architecture · AI APIs and Model Integration · Data Processing and Fine-Tuning · AI Security and Governance · AI applied to DevOps and Software Development

Machine Learning · Deep Learning · Computer Vision · Natural Language Processing · Data Analysis

Data Structures · Algorithms · Object-Oriented Programming · Computer Networks · Information Security · Cloud Computing