AI / ML Engineer
Build systems that learn, reason, and improve from data
¿No sabes si es para ti? Encuentra tu vocación →
0 de 18 misiones hechas
¿Esta carrera es para ti?
Dos minutos antes de dedicarle meses
Marca lo que suena a ti
Marca las que son ciertas para ti.
Habilidades que te llevarás
Trabajos a los que lleva
- ML Engineer
- Data Scientist
- AI Engineer
- Research Engineer
- MLOps Engineer
Antes de empezar
- ✓ Solid Python programming skills
- ✓ High school level statistics (mean, variance, distributions)
18 misiones hasta AI / ML Engineer
4 mundos, 18 misiones. Toca cualquier círculo para abrirlo; el que brilla es el siguiente.
ML is applied mathematics. You need Python fluency and a working grasp of linear algebra, probability, and calculus.
Learn the canonical ML algorithms and the workflow of a real ML project end-to-end.
Learn the architectures powering modern AI: CNNs for vision, Transformers for language.
Deploying a model is only the beginning. Learn to monitor, retrain, and version models reliably.
Más sobre esta carrera
Sobre el trabajo, el salario donde vives, preguntas frecuentes y a dónde puede llevarte esta carrera después.
Sobre el trabajo
Qué harías
- Limpiar y procesar grandes conjuntos de datos con software estadístico
- Crear, probar y validar modelos para que predigan resultados con precisión
- Comparar modelos con medidas de rendimiento, como las funciones de pérdida
- Escribir código en lenguajes de programación para ejecutar análisis y experimentos
- Leer artículos de investigación y trabajos de congresos para seguir los nuevos métodos
- Presentar resultados y recomendar soluciones basadas en datos a los interesados
Dónde trabajarías
- Editoriales, radiodifusión y proveedores de contenidos
- Compañías de seguros
- Empresas de diseño de sistemas informáticos
- Sedes centrales de empresas
Todos los pasos completos
El texto completo de cada paso, para leer o imprimir. Avanza por ellos en el viaje de arriba.
Python, Math & Statistics 4 pasos
Achieve Python proficiency including NumPy, Pandas, and Matplotlib for data manipulation.
Achieve Python proficiency including NumPy, Pandas, and Matplotlib for data manipulation.
Abrir este paso ↑Study linear algebra essentials: vectors, matrices, dot products, eigenvalues.
Study linear algebra essentials: vectors, matrices, dot products, eigenvalues.
Abrir este paso ↑Learn probability theory and statistics: distributions, Bayes theorem, hypothesis testing.
Learn probability theory and statistics: distributions, Bayes theorem, hypothesis testing.
Abrir este paso ↑Understand calculus for ML: gradients, the chain rule, and why they power all neural networks.
Understand calculus for ML: gradients, the chain rule, and why they power all neural networks.
Abrir este paso ↑
Machine Learning Fundamentals 4 pasos
Understand supervised learning: linear regression, logistic regression, decision trees, and SVMs.
Understand supervised learning: linear regression, logistic regression, decision trees, and SVMs.
Abrir este paso ↑Learn unsupervised learning: k-means clustering, PCA for dimensionality reduction.
Learn unsupervised learning: k-means clustering, PCA for dimensionality reduction.
Abrir este paso ↑Practice feature engineering, data pipelines, and cross-validation on real datasets.
Practice feature engineering, data pipelines, and cross-validation on real datasets.
Abrir este paso ↑Compete in a Kaggle beginner challenge to experience the full ML workflow.
Compete in a Kaggle beginner challenge to experience the full ML workflow.
Abrir este paso ↑
Deep Learning & Neural Networks 5 pasos
Learn neural network fundamentals: perceptrons, backpropagation, activations.
Learn neural network fundamentals: perceptrons, backpropagation, activations.
Abrir este paso ↑Use PyTorch (preferred) or TensorFlow to build and train your first networks.
Use PyTorch (preferred) or TensorFlow to build and train your first networks.
Abrir este paso ↑Study CNNs for image recognition, RNNs for sequences.
Study CNNs for image recognition, RNNs for sequences.
Abrir este paso ↑Understand the Transformer architecture (the backbone of GPT, BERT, etc.).
Understand the Transformer architecture (the backbone of GPT, BERT, etc.).
Abrir este paso ↑Fine-tune a pre-trained model (Hugging Face) on a domain-specific task.
Fine-tune a pre-trained model (Hugging Face) on a domain-specific task.
Abrir este paso ↑
MLOps & Production 5 pasos
Learn experiment tracking with MLflow or Weights & Biases.
Learn experiment tracking with MLflow or Weights & Biases.
Abrir este paso ↑Containerize ML models with Docker and serve them via FastAPI or TorchServe.
Containerize ML models with Docker and serve them via FastAPI or TorchServe.
Abrir este paso ↑Build training pipelines using tools like Kubeflow Pipelines or Metaflow.
Build training pipelines using tools like Kubeflow Pipelines or Metaflow.
Abrir este paso ↑Implement data drift monitoring and automated retraining triggers.
Implement data drift monitoring and automated retraining triggers.
Abrir este paso ↑Understand LLM APIs and how Retrieval-Augmented Generation (RAG) works.
Understand LLM APIs and how Retrieval-Augmented Generation (RAG) works.
Abrir este paso ↑
Preguntas frecuentes sobre AI / ML Engineer
Respuestas claras sobre salarios, duración y metodología.
¿Cuánto se tarda en ser AI / ML Engineer?
Esta ruta estima 8–14 months. Tiene 4 fases y 18 pasos, desde «Python, Math & Statistics» hasta «MLOps & Production».
¿Cómo llego a ser AI / ML Engineer?
Sigue las 4 fases en orden, empezando por la fase 1, «Python, Math & Statistics». Tu primera tarea: Achieve Python proficiency including NumPy, Pandas, and Matplotlib for data manipulation.
¿Cuánto gana un AI / ML Engineer?
El sueldo depende del país. El recuadro de salario de esta página muestra la estimación de DraftMyCareer, en moneda local, para el país elegido en el menú.
¿Qué necesito antes de empezar?
Esta ruta indica: Solid Python programming skills; High school level statistics (mean, variance, distributions).
¿Qué habilidades necesita un AI / ML Engineer?
Esta ruta cubre: Python, Statistics, Scikit-learn, PyTorch, MLOps, SQL, Data Preprocessing.
Cambiar de carrera: caminos desde y hacia este rol
Ver todos los cambios →Cómo llegar a este rol desde otros perfiles
- Desde: Backend Developer
Backend developers already have the infrastructure and API skills that most AI prototypes lack, making them prime candidates for lucrative AI Systems Engineering roles.
⏱ 4–6 months🧭 ModerateLo que ya sabesAPI architecture & microservice design · Database query optimization & data modeling · Containerization (Docker) & Linux server management
Ver plan paso a paso
- Month 1: Python Data & LLM Foundations Master Python, NumPy, and call foundation model APIs (OpenAI, Anthropic) with streaming responses and structured JSON outputs.
- Month 2–3: Vector Embeddings & Production RAG Build high-throughput Retrieval-Augmented Generation (RAG) pipelines using pgvector or Qdrant with hybrid search and reranking.
- Month 4–5: Fine-Tuning & Model Evaluation Fine-tune open-weight models (Llama 3 / Mistral) with LoRA/QLoRA and build automated evaluation test suites.
- Month 6: High-Scale AI Microservices Deploy low-latency AI inference microservices with streaming endpoints, token caching, and rate limiting on cloud GPU clusters.
- Desde: Frontend Developer
AI engineers earn substantially higher compensation and build generative apps driving next-generation tech products.
⏱ 6–9 months🧭 ChallengingLo que ya sabesJavaScript / TypeScript · API Consumption & Async Workflows · Component Architecture
Ver plan paso a paso
- Month 1–2: Python & Data Foundations Master Python syntax, NumPy, pandas, and data manipulation workflows.
- Month 3–4: LLM App Architecture Build full-stack AI web apps integrating OpenAI/Anthropic APIs with RAG pipelines.
- Month 5–6: Machine Learning Models Train classification and regression models in Scikit-Learn and PyTorch.
- Month 7+: Production Deployment Deploy LLM agents with latency caching, streaming endpoints, and vector search.
- Desde: Data Analyst
Step into the highest-growth tech domain building transformative AI agents and intelligent systems.
⏱ 6–9 months🧭 ChallengingLo que ya sabesSQL queries · Exploratory data analysis · Metrics definition
Ver plan paso a paso
- Month 1–2: Software Engineering & Python Learn OOP, algorithms, asynchronous programming, and clean code principles.
- Month 3–4: Deep Learning Foundations Study neural network architectures, backpropagation, and PyTorch tensors.
- Month 5–7: LLM System Architecture Construct production RAG architectures, model fine-tuning, and semantic search APIs.
- Month 8+: MLOps & Deployment Automate model registry, tracking (MLflow), and inference pipelines on GPU clusters.