Perfil de habilidad

LLM Fine-tuning

LoRA, QLoRA, PEFT, RLHF, instruction tuning, evaluation

Machine Learning & AI LLM & Generative AI

Roles

donde aparece esta habilidad

Niveles

ruta de crecimiento estructurada

Requisitos obligatorios

los otros 5 opcionales

Machine Learning & AI

LLM & Generative AI

17/3/2026

Selecciona tu nivel actual y compara las expectativas.

Qué se espera en cada nivel

La tabla muestra cómo crece la profundidad desde Junior hasta Principal.

Rol	Obligatorio	Descripción
Data Scientist		Understands the concept of LLM fine-tuning: full fine-tuning vs parameter-efficient methods. Uses Hugging Face API for fine-tuning small models on custom data. Prepares training data in the correct format for various LLM platforms.
LLM Engineer	Obligatorio	Knows LLM fine-tuning basics: full fine-tuning vs LoRA, instruction-tuning data format. Runs basic fine-tuning of a small model via Hugging Face Trainer under mentor guidance.

Rol	Obligatorio	Descripción
Data Scientist		Independently conducts LLM fine-tuning using LoRA, QLoRA, and prefix-tuning. Configures training hyperparameters, monitors loss curves. Evaluates fine-tuned model quality through domain-specific benchmarks and human evaluation.
LLM Engineer	Obligatorio	Independently conducts LLM fine-tuning: LoRA/QLoRA, instruction dataset preparation, hyperparameter tuning. Monitors training via W&B, evaluates results on held-out datasets.

Rol	Obligatorio	Descripción
Data Scientist		Designs fine-tuning pipelines for production LLM systems. Applies RLHF, DPO for model alignment. Optimizes training through DeepSpeed, FSDP. Conducts systematic evaluation via automated benchmarks and red-teaming.
LLM Engineer	Obligatorio	Designs production fine-tuning pipelines: data curation, multi-stage training (SFT → DPO), distributed fine-tuning. Optimizes LoRA rank, learning rate, and batch size for maximum quality.

Rol	Obligatorio	Descripción
Data Scientist		Defines LLM fine-tuning strategy for the organization. Establishes data preparation, training, and evaluation standards for custom LLMs. Coordinates GPU infrastructure and budgets for LLM experiments.
LLM Engineer	Obligatorio	Defines fine-tuning strategy for the LLM team. Establishes best practices for data preparation, training configuration, evaluation. Coordinates fine-tuning experiments and model selection process.

Rol	Obligatorio	Descripción
Data Scientist		Shapes custom LLM development strategy at organizational level. Defines buy vs build for LLM, evaluates open-source vs proprietary models. Influences industry through publications and open-source contributions.
LLM Engineer	Obligatorio	Shapes enterprise fine-tuning platform. Defines approaches to automated fine-tuning, model versioning, and A/B testing of fine-tuned models. Optimizes cost and speed of fine-tuning at scale.

Junior 2 requisitos

Data Scientist

Understands the concept of LLM fine-tuning: full fine-tuning vs parameter-efficient methods. Uses Hugging Face API for fine-tuning small models on custom data. Prepares training data in the correct format for various LLM platforms.
LLM Engineer
Obligatorio

Knows LLM fine-tuning basics: full fine-tuning vs LoRA, instruction-tuning data format. Runs basic fine-tuning of a small model via Hugging Face Trainer under mentor guidance.

Middle 2 requisitos

Data Scientist

Independently conducts LLM fine-tuning using LoRA, QLoRA, and prefix-tuning. Configures training hyperparameters, monitors loss curves. Evaluates fine-tuned model quality through domain-specific benchmarks and human evaluation.
LLM Engineer
Obligatorio

Independently conducts LLM fine-tuning: LoRA/QLoRA, instruction dataset preparation, hyperparameter tuning. Monitors training via W&B, evaluates results on held-out datasets.

Senior 2 requisitos

Data Scientist

Designs fine-tuning pipelines for production LLM systems. Applies RLHF, DPO for model alignment. Optimizes training through DeepSpeed, FSDP. Conducts systematic evaluation via automated benchmarks and red-teaming.
LLM Engineer
Obligatorio

Designs production fine-tuning pipelines: data curation, multi-stage training (SFT → DPO), distributed fine-tuning. Optimizes LoRA rank, learning rate, and batch size for maximum quality.

Lead / Staff 2 requisitos

Data Scientist

Defines LLM fine-tuning strategy for the organization. Establishes data preparation, training, and evaluation standards for custom LLMs. Coordinates GPU infrastructure and budgets for LLM experiments.
LLM Engineer
Obligatorio

Defines fine-tuning strategy for the LLM team. Establishes best practices for data preparation, training configuration, evaluation. Coordinates fine-tuning experiments and model selection process.

Principal 2 requisitos

Data Scientist

Shapes custom LLM development strategy at organizational level. Defines buy vs build for LLM, evaluates open-source vs proprietary models. Influences industry through publications and open-source contributions.
LLM Engineer
Obligatorio

Shapes enterprise fine-tuning platform. Defines approaches to automated fine-tuning, model versioning, and A/B testing of fine-tuned models. Optimizes cost and speed of fine-tuning at scale.

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