AI EngineeringCurriculum in development
LLM Engineer
Prepare datasets, adapt language models, evaluate behavior, and optimize inference.
Designed forML and AI engineers specializing in language-model development and adaptation.
Recommended foundationPython, PyTorch fundamentals, machine-learning evaluation, and familiarity with transformer models.
Problems you will learn to solve
Work from real constraints, not generic tool demonstrations.
Build a trustworthy adaptation dataset
Measure whether fine-tuning improved target behavior
Serve an adapted model within latency and cost constraints
Working environment
Python, PyTorch, Transformers, PEFT, TRL, Unsloth, vLLM, Hugging Face, evaluation suites, GPUs
System mapAn LLM adaptation workflow
Training dataset
Data preparation
SFT + LoRA
Preference tuning
Model evals
Inference optimization
Product integration
Provisional curriculum
Six connected modules
The sequence will be validated with practitioners before enrollment opens.
- 01Transformer and tokenization foundations
- 02Dataset design and quality
- 03Supervised fine-tuning and LoRA
- 04Preference data, DPO, and verifiable-reward RL (GRPO)
- 05Behavior evaluation and safety
- 06Inference optimization and integration
Planned capstone
Finish with evidence of applied skill.
An adapted language model with documented data, reproducible training, behavior evaluations, and an inference endpoint.
Focused lessonsUnderstand the underlying ideas
Guided practiceWork through realistic constraints
Applied projectProduce a demonstrable result
Structured reviewRevise the work after feedback
Program updates
Register your interest in LLM Engineer.
This is not enrollment and no payment is required. We will use your response to validate demand and contact you when the curriculum and cohort details are ready.