Software Engineering & Computer Science student (2025 - 2029) focused on building high-performance, production-grade systems across the full stack.

I work with an engineering mindset, combining modern interfaces with robust, scalable backends guided by Clean Architecture, Domain-Driven Design (DDD), and SOLID principles.

My primary stacks are:

• TypeScript ecosystem: Node.js, React, Next.js, NestJS
• Java ecosystem: Spring Boot, Spring Cloud, Hibernate/JPA
• Python & Go: automation, data workflows, concurrency-critical services

Currently seeking internship opportunities to apply modern web development, cloud-native delivery, and AI/ML to real-world systems.

const profile = {
  name: "Emanuel Lázaro",
  title: "Software Engineering Student & Full Stack Developer",

  stacks: {
    typescript: ["Node.js", "React", "Next.js", "NestJS"],
    java:       ["Spring Boot", "Spring Cloud", "Hibernate/JPA", "Maven/Gradle"],
    python:     ["FastAPI", "PyTorch", "TensorFlow", "Pandas"],
    go:         ["Gin", "gRPC", "Concurrency Patterns"],
  },

  education: {
    field:  "Software Engineering & Computer Science",
    period: "2025–2029",
  },

  coreFocus: [
    "Scalable & High-Performance Web Systems",
    "Cloud-Native Architecture and Delivery",
    "AI/ML Integration in Production Systems",
    "Enterprise Java Backend with Spring Ecosystem",
  ],

  engineeringPrinciples: [
    "Clean Architecture",
    "Domain-Driven Design (DDD)",
    "SOLID Principles",
    "Test-Driven Development (TDD)",
  ],
};

Full-Stack Systems React · Next.js · Node.js · NestJS

Java / Spring Backend Spring Boot · Spring Cloud · JPA

Cloud & DevOps Docker · Kubernetes · CI/CD · AWS

AI/ML Integration Python · PyTorch · Hugging Face

I optimize for reliability and long-term maintainability by applying proven principles across both TypeScript and Java ecosystems:

• Clean Architecture: explicit boundaries, testable business rules, infrastructure adapters
• DDD: ubiquitous language, domain modeling, bounded contexts, and clear separation of concerns
• SOLID: small units with cohesive responsibilities and stable contracts
• TDD: test-first thinking with JUnit 5 (Java) and Jest/Vitest (TypeScript)
• System design: decomposition, backpressure, caching, failure-aware design, and event-driven patterns

flowchart LR
  U[👤 Users] --> E[Edge / CDN]
  E --> FE[Frontend\nNext.js / React]
  FE --> GW[API Gateway]

  GW --> STS[Service A\nNestJS / Node.js]
  GW --> SJV[Service B\nSpring Boot / Java]
  GW --> SGO[Service C\nGo / Python]

  STS --> DB1[(PostgreSQL)]
  STS --> CA[(Redis Cache)]
  SJV --> DB2[(MySQL / PostgreSQL)]
  SJV --> MQ[📨 Kafka / RabbitMQ]
  SGO --> DB3[(MongoDB)]
  SGO --> ML[🤖 ML Inference]

  MQ --> STS
  MQ --> SGO

  CI[⚙️ CI/CD] --> REG[Container Registry]
  REG --> K8s[☸️ Kubernetes]
  K8s --> STS
  K8s --> SJV
  K8s --> SGO

@RestController
@RequestMapping("/api/v1/projects")
@RequiredArgsConstructor
public class ProjectController {

    private final ProjectService projectService;

    @GetMapping("/{id}")
    public ResponseEntity<ProjectDTO> findById(@PathVariable UUID id) {
        return ResponseEntity.ok(projectService.findById(id));
    }

    @PostMapping
    @PreAuthorize("hasRole('DEVELOPER')")
    public ResponseEntity<ProjectDTO> create(@Valid @RequestBody CreateProjectRequest request) {
        ProjectDTO created = projectService.create(request);
        URI location = ServletUriComponentsBuilder.fromCurrentRequest()
            .path("/{id}").buildAndExpand(created.id()).toUri();
        return ResponseEntity.created(location).body(created);
    }
}

Machine Learning & Data Science competitor on Kaggle. I use competitions to benchmark models, refine feature engineering, and stress-test applied ML workflows.

Technical Focus

• Training Deep Learning models with PyTorch / TensorFlow
• Mathematical foundations: linear algebra, multivariable calculus, probability & statistics
• Feature engineering and large-scale data processing with Pandas / NumPy
• Computer Vision and NLP for unstructured problems
• Fine-tuning and deploying models from Hugging Face Hub
• Continuous study of SOTA architectures and modern training techniques

💡 I'm especially interested in teams that value engineering craftsmanship, clear architecture, and continuous learning, whether in TypeScript, Java, Go, or Python-based stacks.

     

Building scalable systems with clean interfaces, robust backends, and measurable outcomes.