GenAI Data Engineering
Build RAG data pipelines for ingestion, chunking, embedding, and indexing. Manage vector store operations and embedding model lifecycle.
What a GenAI data engineer does · GenAI Data Engineering
Retrieval is only as good as the pipeline that fed it. GenAI data engineering owns that pipeline end to end: document ingestion at scale, chunking strategies that respect structure, embedding jobs and the embedding-model lifecycle, vector stores in pgvector and knowledge graphs in Neo4j, orchestration with Argo Workflows and Kafka, versioning with DVC, and data-quality controls that stop a bad crawl from poisoning every answer downstream. Multi-tenant isolation and pipeline-health monitoring are part of the job because the same infrastructure serves many teams. Think of it as the data platform beneath application engineering and agent engineering: they consume what you index, and they notice first when it is wrong.
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8 courses, each building on the last. The bar shows each course's share of the curriculum. 35 lessons are free to read.
Python Essentials for Agent Builders 11.2% of the curriculum
In scope for GenAI Data Engineering: chapters 1–13 — 62 of this path’s 554 lessons. 1 free GenAI Data Engineering lesson
LLM Foundations for Agent Builders 10.8% of the curriculum
In scope for GenAI Data Engineering: chapters 1–7, 14, 17–20 — 60 of this path’s 554 lessons. 1 free GenAI Data Engineering lesson
Kubernetes Essentials for GenAI Engineers 10.8% of the curriculum
In scope for GenAI Data Engineering: chapters 1–6, 8, 10–12 — 60 of this path’s 554 lessons. 1 free GenAI Data Engineering lesson
Web APIs & Services for GenAI Engineers 9.7% of the curriculum
In scope for GenAI Data Engineering: chapters 1–7, 9–10 — 54 of this path’s 554 lessons. 1 free GenAI Data Engineering lesson
Data Infrastructure Essentials for GenAI 10.8% of the curriculum
- Neo4j Graph Database
- Data Pipeline Orchestration
- Data Quality & Validation
- Data Infrastructure Operations
- 4 of 10 chapters shownand 6 more
Enterprise LLM Customization 20.6% of the curriculum
- Model Selection Engine
- Production RAG Pipeline
- Vector Database Engineering
- LangGraph Agentic Orchestration
- GraphRAG & Knowledge Graphs
- Agent Memory & Stateful Systems
- 6 of 19 chapters shownand 13 more
GenAI Data Pipelines 19.5% of the curriculum
- Document Ingestion with VLMsExtract documents using Docling's unified multi-format parserUse Google Document AI for managed OCR and layout parsingDesign a unified document model normalizing all extraction outputsBuild a routing system selecting the optimal extraction method
- Data Cleaning & Quality AgentsImplement content quality scoring with NeMo Curator filters
- Chunking & Contextual RetrievalBuild Anthropic's Contextual Retrieval pattern
- Context Engineering & LLM EnrichmentExtract structured metadata using Instructor with Pydantic schemas
- Multi-Format & Multimodal ProcessingDesign multi-format storage strategies on GCS and PostgreSQL
- Embedding Model Selection & BenchmarkingBuild an embedding benchmarking frameworkEvaluate Voyage 4's shared embedding space across model tiersImplement an embedding abstraction layer with provider switching
- Embedding Pipelines with Cost ControlsBuild embedding pipelines with LiteLLM gateway routingTrack costs in real-time with Langfuse and enforce budgetsOrchestrate pipelines as Argo Workflows with Kafka triggers
- Vector Store Operations on AlloyDBConfigure AlloyDB with pgvector and ScaNN indexingBuild zero-downtime reindexing for embedding model upgrades
- Hybrid Search, Reranking & CachingBuild semantic caching using Redis LangCache
- Knowledge Graph Construction with LightRAGRoute extraction by complexity to cost-effective models
- Entity Resolution & Linking
- Agentic Graph-RAG PipelinesBuild agentic RAG with query decomposition and self-verification
- Knowledge Graph MaintenanceBuild automated maintenance workflows with Argo
- Evaluation-Driven Quality EngineeringInstrument pipelines with OpenTelemetry GenAI conventions
- PII Detection, Guardrails & ComplianceImplement Presidio regex and NER-based PII detectionAdd NeMo Curator PII redaction for pipeline-scale detectionDeploy NeMo Guardrails for output safety and validation
- Agentic Pipeline OrchestrationBuild event-driven triggers with Kafka and KEDA autoscalingVersion datasets with DVC backed by GCSConnect pipeline agents via MCP for autonomous orchestrationImplement pipeline observability with OTel, Prometheus, Grafana
- Data Flywheels & Continuous ImprovementImplement model cascading for cost reduction
- Production Capstone on GKEDesign end-to-end architecture on GKE AutopilotDeploy infrastructure with Crossplane + Helm + Kustomize
GenAI Operations 6.5% of the curriculum
In scope for GenAI Data Engineering: chapters 38–43 — 36 of this path’s 554 lessons.
Core responsibilities this discipline prepares you for. Open one to see how you build it.
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
Each group becomes a signed node on your public skill graph — a recruiter-auditable record of the labs you actually passed. Share the URL; they see the discipline, the labs, and the signature.
Run the embedding pipeline at corpus scale: batch generation, and the senior signal — re-embedding the whole corpus to a new model with zero downtime (dual-write, backfill, cutover), plus dimensionality/quantization and cost trade-offs.
Get heterogeneous sources in cleanly — where the job actually bleeds time: PDF/HTML/table/multimodal parsing, cleaning and normalization, dedup, and incremental/CDC sync with freshness handling.
Operate the index as a lifecycle: vector-DB ops, index construction and tuning (HNSW/IVF/PQ), reindex/rebuild/compaction/backup, sharding, and recall-vs-latency tuning at build time.
Is the index good? precision@k / recall@k / MRR / nDCG, golden-retrieval-set construction, chunking-strategy tuning (tuned against these metrics), drift detection, and retrieval regression — measuring the index, not the answer.
Build the graph when vectors aren't enough: entity/relation extraction, ontology and graph construction (Neo4j), GraphRAG retrieval, and graph+vector hybrid.
Build and tune the retrieval stage offline: dense+sparse/BM25 fusion, reciprocal rank fusion, and cross-encoder reranking — tuned against a golden set, not per-request in a feature.
Close the loop: feedback capture, re-embedding/re-index triggers, active learning, corpus expansion, and continuous retrieval eval.
Keep the corpus compliant: PII detection/redaction, lineage, access governance, and the hard problem — right-to-be-forgotten deletion from the index and every derived artifact.
Run the pipeline as a system: orchestration (Airflow/Dagster/Prefect), batch-vs-streaming, scheduling and backfills, data versioning, deployment/CI-CD for pipeline jobs, and monitoring/scaling.
Baseline provider access in pipeline code: embedding and LLM SDK calls, auth, batching, and retries.
Production Python for pipeline code: async, Pydantic, data libraries, typing, and error handling.