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GenAI Data Engineering

L4-L5 · 234h · 7 courses · 78 chapters

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

Role-alignedHands-on labsCapstone project30-day money-back

What you'll own in this role

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

Tools you'll ship with

Industry-standard stack for current L4–L6 GenAI engineering roles.

KafkaPostgreSQLpgvectorNeo4jMinIORedisArgo WorkflowsDVCPandasSparkAirflowHuggingFace

Your learning route

7 courses · sequenced for compounding · 78 chapters · ~234 hours

Step 1 · Foundations

Python Essentials for Agent Builders

13 chapters

Step 2

LLM Foundations for Agent Builders

20 chapters

Step 3

Kubernetes Essentials for GenAI Engineers

17 chapters

Step 4

Web APIs & Services for GenAI Engineers

12 chapters

Step 5

Data Infrastructure Essentials for GenAI

10 chapters

Step 6

Enterprise LLM Customization

11 chapters

Step 7 · Capstone

GenAI Data Pipelines

6 chapters

Start the GenAI Data Engineering discipline today

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