Skill Profile

LLM Fine-tuning

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

Machine Learning & AI LLM & Generative AI

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 5 optional

Machine Learning & AI

LLM & Generative AI

3/17/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
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	Required	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.

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

Role	Required	Description
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	Required	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.

Role	Required	Description
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	Required	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.

Role	Required	Description
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	Required	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 requirements

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
Required

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 requirements

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
Required

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 requirements

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
Required

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 requirements

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
Required

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 requirements

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
Required

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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