Software Engineer building AI-enabled products, analytics platforms, and resilient full-stack systems.
Current focus in 2026: AI-native engineering workflows, applied LLM features, and scalable cloud systems that turn complex operational data into usable products.
I work at the intersection of software engineering, architecture, and practical AI adoption. My focus is building systems that are scalable, maintainable, and useful in the real world, especially where analytics, data-heavy workflows, cloud infrastructure, and applied AI need to work together cleanly.
- Around 5 years building web applications, backend services, analytics systems, internal tools, and AI-enabled products.
- Full-stack range across React, Node.js, .NET, PostgreSQL, Redis, Docker, and Linux.
- Stronger-than-average systems judgment: architecture trade-offs, reliability, performance, maintainability, and delivery quality.
- Multi-cloud experience across AWS, Azure, and GCP.
- Practical AI experience across LLM-powered features, automation workflows, prompt engineering, RAG-oriented thinking, and AI-augmented development.
| Area | How I work in 2026 |
|---|---|
| Discovery | Break down ambiguous problems, define technical constraints early, and choose the smallest path that validates the right assumption. |
| Delivery | Use AI assistants such as Claude Code and GitHub Copilot to accelerate prototyping, refactoring, debugging, and implementation. |
| Quality | Keep engineering judgment human-led: review output carefully, preserve architecture coherence, and optimize for maintainability over novelty. |
| Communication | Prefer human-readable PRs, practical documentation, and clear technical reasoning over black-box delivery. |
| Product | Apply AI where it creates real leverage: faster workflows, better UX, clearer insight extraction, and lower operational friction. |
| Category | Stack | Why I use it |
|---|---|---|
| Frontend | React, TypeScript, dashboards, responsive interfaces | For building operational products where clarity, speed, and maintainable UI architecture matter. |
| Backend | Node.js, C#/.NET, REST APIs, asynchronous workflows | For reliable business logic, integrations, and systems that need to scale without becoming brittle. |
| Data | PostgreSQL, Redis, query optimization, caching | For data-heavy applications that need predictable performance and clean modeling. |
| Cloud | AWS, Azure, GCP, Docker, Linux, CI/CD | For production environments that require flexibility, observability, and sustainable delivery. |
| AI | LLM APIs, LangChain, prompt engineering, RAG concepts, AI-assisted workflows | For building practical AI features and increasing engineering throughput without sacrificing quality. |
| Engineering | Code review, debugging, architecture, testing, systems integration | For keeping delivery quality high as products and teams become more complex. |
- Senior Software Developer working on internal platforms for infrastructure analysis, monitoring, analytics, dashboards, and automated processing.
- Contributed to backend architecture, APIs, async workflows, query optimization, Redis caching, and geospatial features.
- Helped improve scalability, responsiveness, reliability, and long-term maintainability across multiple internal applications.
- Full Stack Software Developer working on internal systems, dashboards, analytics, integrations, and decision-support workflows.
- Delivered across frontend, backend, APIs, databases, cloud environments, and production support.
- Grew into stronger ownership around system quality, debugging, performance, and engineering structure.
- Interactive chatbot integrated into a map-based application for contextual search, filtering, highlighting, and layer control.
- Data analysis chatbot capable of generating charts, summaries, reports, and calculations from structured information.
- Gym platform with admin dashboard and mobile app, including AI-generated workouts and diet features.
- Automation flows connected to CRM and advertising APIs for sales and marketing operations.
- Build for long-term maintainability, not short-term noise.
- Treat AI as a force multiplier, not a substitute for engineering judgment.
- Prefer clarity in architecture, code review, and documentation.
- Balance delivery speed with reliability, security, and product value.
- Improve systems by reducing friction, technical risk, and unnecessary complexity.
- GitHub: github.com/felipedev1
- LinkedIn: linkedin.com/in/felipe-pereira-dev1
- Email: felipe.pereira.dev@gmail.com
- Location: Brazil
I am especially interested in roles and projects where software engineering fundamentals, cloud architecture, data-intensive systems, and practical AI adoption need to come together in a way that produces measurable business value.