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GenAI Data Engineering

L4-L5 · 7 courses · 78 chapters

Build RAG data pipelines for ingestion, chunking, embedding, and indexing. Manage vector store operations and embedding model lifecycle.

What you'll learn

Core responsibilities this discipline prepares you for.

Build embedding pipelines

— ingest, chunk, embed, and store in vector databases

Select and benchmark embedding models across OpenAI and Gemini for domain-specific accuracy
Implement chunking strategies (fixed, semantic, recursive) with batch embedding generation
Build complete pipelines processing thousands of documents into pgvector with HNSW indexing

Design RAG data infrastructure

— hybrid search and reranking

Build BM25 + semantic hybrid search with LLM-as-reranker patterns using Gemini
Implement semantic caching for throughput optimization and query result deduplication
Construct hybrid search pipelines and benchmark retrieval quality with RAGAS precision-recall metrics

Build knowledge graph pipelines

using Neo4j

Extract entities from unstructured text and construct knowledge graphs with relationship typing
Implement GraphRAG patterns and agentic Graph-RAG with MCP tool integration for graph traversal
Build knowledge graphs from document corpora and query them with graph-aware retrieval agents

Process documents at scale

— parsing, chunking, and quality filtering

Process multi-format documents with Docling across PDF, HTML, and Office formats
Apply intelligent context-preserving chunking and GPU-accelerated curation with NeMo Curator
Build document processing pipelines that handle real-world messy data with quality filtering

Implement data quality controls

— PII, dedup, compliance filtering

Integrate Presidio for PII detection with custom entity recognizers and deduplication strategies
Build compliance pipelines with content classification for regulated industries
Construct quality gates that block non-compliant documents from entering the embedding pipeline

Orchestrate data pipelines

with scheduling and failure recovery

Use Argo Workflows for Kubernetes-native pipeline orchestration with DVC data versioning
Build quality gates between pipeline stages with dead-letter queues and failure recovery patterns
Wire multi-stage pipelines with automatic retry, checkpoint recovery, and quality validation gates

Monitor pipeline health

— freshness, quality scores, embedding drift

Instrument pipeline stages with OpenTelemetry and build Grafana dashboards for freshness and quality
Monitor retrieval quality continuously with RAGAS evaluation and embedding drift detection
Build monitoring for live pipelines that detects data quality degradation and triggers remediation

Design multi-tenant data isolation

for enterprise RAG

Build tenant-aware embedding pipelines with pgvector namespace isolation per customer
Implement row-level security for vector search with per-tenant quality monitoring
Verify tenant data isolation under concurrent multi-tenant queries with cross-tenant leakage tests

Your learning path

7 courses · sequenced for compounding · 78 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate12 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Intermediate10 Ch

Step 5

Data Infrastructure Essentials for GenAI

Kafka, pgvector, object stores, and data pipelines — the storage spine under every production GenAI system.

Advanced11 Ch

Step 6

Enterprise LLM Customization

Customize LLMs for enterprise — prompt engineering, RAG at scale, fine-tuning, and domain adaptation techniques.

Advanced6 Ch

Capstone

GenAI Data Pipelines

End-to-end data pipelines for GenAI — ingestion, transformation, vectorization, versioning, and lineage.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

Kafka

Streaming ingestion for GenAI pipelines

PostgreSQL

Core warehouse for feature stores

pgvector

Embedding store for RAG feature sets

Neo4j

Entity graphs as feature enrichment

MinIO

S3-compatible raw-data lake

Redis

Online feature lookups at inference time

Argo Workflows

DAG-based pipeline orchestration

DVC

Version datasets and model artifacts

Pandas

Data prep at sub-100GB scale

Spark

Data prep at 100GB+ scale

Airflow

Schedule recurring training + eval

HuggingFace

Pull, tune, and push models in labs

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