LLM Pipeline Engineering

Train and ship production LLMs — from inference infra and dataset curation to fine-tuning, alignment, and serving.

Anyone can call an LLM API. The teams that own their models — pick the GPU, curate the corpus, fine-tune for the domain, align for behavior, and serve at scale — set the ceiling for what their product can do.

What you’ll be able to do

Stand up production LLM inference (vLLM, KV cache, GPU memory math)
Curate, dedupe, and quality-score training corpora at scale
Run fine-tuning jobs (LoRA, full FT, DPO/RLHF) with measurable lift
Deploy and operate an LLM serving platform with cost and quality SLOs

Curriculum

Phase 1: LLM Foundations

Stand up real LLM infrastructure. GPU memory math, vLLM serving, KV cache, batching — the systems layer most prompt-engineering tutorials skip.

LLM Inference Infrastructure

VRAM math (weights + activations + KV cache + grads), GPU sizing, vLLM continuous batching, paged attention, and the throughput-vs-latency tradeoffs that decide what hardware bill your team actually pays.

Training Data Engineering

Crawl-to-corpus: source curation, language and quality filters, MinHash/LSH dedup, contamination checks, tokenizer fit, and the dataset-versioning hygiene you need before any training run is reproducible.

Phase 2: Train & Adapt

Turn raw text and instructions into a model that does what you need. Instruction tuning, synthetic data, LoRA vs full FT — and how to measure that a fine-tune actually moved the needle.

Instruction Dataset Design

How to design instruction-tuning data that actually shifts behavior — task taxonomy, prompt diversity, response quality grading, deduplication of near-clones, and the eval set you build before you train, not after.

Synthetic Data Generation

Self-instruct, evol-instruct, distillation, and persona-driven generation. When synthetic data helps, when it collapses your model, and the contamination + diversity controls that keep it useful.

Fine-Tuning (LoRA, Full, DPO)

LoRA vs full-FT decision tree (task shift size, compute budget), QLoRA + 4-bit quantization, learning-rate schedules that don't blow up, eval-driven checkpoint selection, and the failure modes (catastrophic forgetting, mode collapse) you need to monitor.

Phase 3: Production LLMs

Aligning, serving, and operating LLMs in production. RLHF/DPO loops, multi-tenant inference platforms, cost guardrails, and the runbooks that keep an LLM service alive on-call.

Alignment (RLHF / DPO)

Reward modeling, PPO and DPO loops, preference dataset construction, alignment-vs-capability tradeoffs, and the safety evals you run before pushing an aligned model to a real user.

Production LLM Serving

Multi-tenant inference topology (vLLM clusters, autoscaling, KV-cache reuse), request routing, semantic caching, fallback cascades, latency/cost SLOs, and on-call observability for an LLM service.

LLM Platform Capstone

End-to-end build: pick a domain, curate the corpus, fine-tune the model, align it, serve it, monitor it. Defended in an architecture review with explicit team contracts and ADRs.

What you’ll build

vLLM-backed inference service with GPU memory budgeting
Training-corpus pipeline with quality scoring + dedup
Fine-tuning + alignment loop (LoRA / DPO) with eval gates
Production LLMOps stack: routing, caching, cost/quality monitoring

Wrapping an API works in a demo… but breaks the second your product needs its own model.

Without the full pipeline, you'll hit:

Inference bills that scale faster than revenue, and no levers to pull
Fine-tunes that look better on eval but worse for users
Training corpora with leakage, contamination, or silent quality drift
Alignment that fixes one behavior and regresses three others
A serving stack that can't survive a 10× traffic spike or a model swap

What is LLM Pipeline Engineering?

LLM pipeline engineering is the practice of building production systems around a model you own — inference infrastructure, training-corpus curation, instruction-tuning, fine-tuning, alignment, and serving. It's the difference between a team that calls a hosted API and a team that ships, fine-tunes, and operates its own model.

Why this matters in production

Calling an LLM API is a starting point, not a moat. Teams that own the pipeline — picking the GPU, building the corpus, running the fine-tune, aligning the model, and serving it under SLO — control their own roadmap. Without the full pipeline, every quality, cost, and behavior decision is gated on someone else's model release.

Common use cases

Sizing GPUs and inference infrastructure for a target QPS and latency
Building training-corpus pipelines with quality scoring and deduplication
Designing instruction-tuning datasets that shift model behavior on real tasks
Running LoRA / full fine-tunes with eval-gated checkpoint selection
Aligning a fine-tuned model with DPO or RLHF for production safety
Operating a multi-tenant LLM serving platform with cost and quality SLOs

LLM vs alternatives

LLM vs Hosted APIs

Hosted APIs (OpenAI, Anthropic) are great defaults. LLM pipeline engineering is what you do when cost, latency, behavior, or data-locality requirements push you to own inference and training. Most teams use both — APIs for general tasks, owned models for the parts of the product they need to control.

LLM vs RAG

RAG retrieves context at query time. LLM pipeline engineering changes the model itself — through fine-tuning, alignment, and serving infrastructure. RAG handles dynamic knowledge; pipeline engineering handles persistent behavior, cost, and ownership. Production systems use both.

LLM vs MLOps

MLOps covers the full ML lifecycle (training, deployment, monitoring) for any model. LLM pipeline engineering is the LLM-specific specialization — GPU memory math, fine-tuning loops, alignment, and the inference patterns (batching, KV cache, paged attention) unique to autoregressive models.

Related skills

RAG Systems — RAG is a complementary pattern — knowledge retrieval that pairs with the owned models built in
LLM Evaluation — Fine-tuning and alignment decisions are gated on evaluation frameworks from
AI Inference & Serving — Serving infrastructure deepens the inference patterns introduced in

Why this skill matters

LLM pipeline engineering is the bridge from 'AI consumer' to 'AI builder.' This skill proves you can train, align, and operate a model end-to-end — the difference between a team that calls an API and a team that ships its own.

Common questions about LLM

What is LLM pipeline engineering?

LLM pipeline engineering is the end-to-end practice of training, aligning, and serving large language models in production — covering inference infrastructure, training-corpus curation, fine-tuning, alignment, and serving. It's what teams do when they need to own the model behind their product.

Is this about prompt engineering?

No. Prompt engineering is the API-call layer; this curriculum starts where prompts run out — when you need to fine-tune, align, or serve your own model. Prompts are still useful, but they aren't the bottleneck this path teaches you to break through.

Do I need to fine-tune to ship an AI product?

Not always. Most products start with hosted APIs + RAG. You move into pipeline engineering when API costs, latency, behavior, or data-locality requirements force you to own more of the stack.

How long does this curriculum take?

About 24 hours for the core lessons across 8 modules. End-to-end builds (especially the capstone) take longer because GPU runs and fine-tuning loops are real, not simulated.

LoRA or full fine-tuning?

LoRA when the task shift is moderate, the compute budget is small, and you need to ship multiple adapters. Full fine-tuning when the task shift is large or you'll deploy one model. Pillar 5 walks through the decision rule with worked examples.

RAG vs fine-tuning?

RAG for dynamic knowledge and citations. Fine-tuning for persistent behavior, style, and domain expertise. Most production systems combine both — fine-tune the model, then RAG over your data on top.

Do I need a GPU to do this?

For inference and small fine-tunes, a single A100 or rented H100 is enough. For larger fine-tunes, the curriculum walks through cloud GPU tradeoffs (Lambda, Modal, Coreweave) so you can run the lessons without owning hardware.