ML Engineer
Building, training, and deploying machine learning models to production
ML Engineer es un rol en la familia ML & AI Engineering. Tiene 58 habilidades en 5 niveles (de Junior a Principal). 125 habilidades son obligatorias. Dominios clave: Programming Fundamentals, Backend Development, Database Management.
Stack tecnológico
Enfoque por nivel
Training models using existing pipelines. Feature engineering. Model validation. Preparing datasets. Working with Jupyter notebooks.
Designing ML pipelines. Model selection and tuning. A/B testing models. Deploying models to production. Feature store.
ML systems architecture. Inference optimization (ONNX, TensorRT). Designing real-time ML. Researching new approaches. Mentoring.
ML platform strategy. MLOps infrastructure. Coordinating ML and backend. Experimentation standards. ML team roadmap.
Company AI strategy. LLM integration. ML at scale. Research agenda. Publications and talks.
Matriz de habilidades
58 habilidades × 5 niveles. Haga clic en una celda para ver detalles.
AI-Assisted Development
4 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| GitHub Copilot | A | A | W | A | A |
| Cursor IDE | A | W | A | A | — |
| ChatGPT / Claude | A | A | W | A | A |
| Prompt Engineering for Code | A | A | W | A | A |
API & Integration
5 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| REST API Design | A | W | A | A | E |
| GraphQL Design | A | W | A | E | E |
| WebSocket API Design | A | W | A | E | E |
| gRPC & Protocol Buffers | A | A | W | A | A |
| API Documentation | A | W | A | E | E |
Architecture & System Design
1 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| System Design Fundamentals | A | W | A | E | E |
Backend Development
4 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Python Web Frameworks | A | W | A | A | E |
| Apache Kafka | A | A | W | A | A |
| Redis | A | A | W | A | A |
| Task Queues | A | A | W | A | A |
Cloud & Infrastructure
5 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Docker | A | W | A | A | E |
| Kubernetes Core | A | W | A | A | E |
| Terraform | A | W | A | E | E |
| AWS | A | W | A | A | E |
| Network Fundamentals | A | W | A | E | E |
Data Engineering
4 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Apache Spark | A | W | A | E | E |
| Pandas / Polars | A | W | A | E | E |
| SQL-based ETL | A | W | A | E | E |
| Data Quality | A | W | A | E | E |
Database Management
4 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| PostgreSQL | A | W | A | A | E |
| Database Indexing | A | A | W | A | A |
| Query Optimization | A | A | W | A | A |
| Data Modeling & Schema Design | A | W | A | E | E |
DevOps & CI/CD
1 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| GitHub Actions / GitLab CI | A | W | A | A | E |
Machine Learning & AI
9 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Classical ML (scikit-learn) | A | W | A | E | E |
| Gradient Boosting | A | W | A | E | E |
| PyTorch | A | W | A | E | E |
| MLflow | A | W | A | E | E |
| Feature Stores | A | W | A | E | E |
| Model Serving | A | W | A | E | E |
| Experiment Tracking | A | W | A | E | E |
| ML Pipelines | A | W | A | E | E |
| Model Monitoring | A | W | A | E | E |
Observability & Monitoring
4 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Structured Logging | W | A | E | — | — |
| Prometheus & Grafana | A | W | A | — | — |
| OpenTelemetry | A | W | A | E | E |
| SLI / SLO / SLA | A | W | A | E | E |
Programming Fundamentals
9 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Algorithms & Complexity | W | W | A | A | E |
| Data Structures | W | W | A | A | E |
| OOP & SOLID Principles | W | W | A | A | E |
| Design Patterns | W | W | A | A | E |
| Multithreading | W | W | A | A | E |
| Async Programming | W | W | A | A | E |
| Code Quality & Refactoring | W | W | A | A | E |
| Type Safety & Type Systems | A | W | A | E | E |
| Memory Management | A | W | A | E | E |
Security
3 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| OWASP & Application Security | A | W | A | E | E |
| Secure Coding Practices | A | W | A | E | E |
| JWT / OAuth2 / OIDC | A | W | A | E | E |
Testing & QA
3 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Unit Testing | A | W | A | A | E |
| Integration Testing | A | W | A | A | E |
| E2E Testing | A | W | A | E | E |
Version Control & Collaboration
2 habilidades| Habilidades | Jun | Mid | Sen | Lead | Princ |
|---|---|---|---|---|---|
| Git Advanced | A | W | A | A | E |
| Code Review | A | W | A | A | E |
Preguntas frecuentes
¿Qué habilidades se necesitan para el rol de ML Engineer?
El rol de ML Engineer requiere 58 habilidades, de las cuales 125 son obligatorias. Las habilidades se distribuyen en 5 niveles: de Junior a Principal. Ver matriz completa.
¿Cómo avanzar al siguiente nivel en el rol de ML Engineer?
Use la Calculadora de grado para evaluar su nivel actual y obtener recomendaciones personalizadas.
¿Qué stack tecnológico se usa en el rol de ML Engineer?
El stack incluye 5 tecnologías en diferentes niveles. Python 3.11+, scikit-learn, pandas/numpy, Jupyter, PyTorch/TensorFlow basics, SQL, Git, Python 3.12+, PyTorch/TensorFlow, XGBoost/LightGBM/CatBoost, MLflow, Airflow, Feature store basics, Docker, DVC, PyTorch advanced, ONNX/TensorRT, Triton Inference Server, Kubeflow/MLflow, Spark ML, Ray, Kubernetes, Prometheus/Grafana...
¿Cómo define la comunidad los requisitos para el rol de ML Engineer?
Los requisitos del rol son definidos por la comunidad a través de un sistema de propuestas. Cualquier miembro puede sugerir cambios que pasan por votación y revisión de expertos.