AI Data Engineer — Inference Phase

🗄️

Inference Specialization

AI Data Engineer

AI Data Engineers build pipelines, feature stores, and data quality systems that feed ML models with clean, timely, and well-labelled data. Often the unsung heroes of AI — and increasingly critical as data quality becomes the primary bottleneck for model performance.

Days

Projects

$160K

Median

#1 Bottleneck

in Production AI

$120K–$210K

Salary Range

Week 1: Modern Data Engineering Stack · Days 1–8  

Master the modern data stack for AI: batch and streaming pipelines, lakehouses, and automated data quality gates.

D1–3

Modern data stack for AI: dbt, Airflow, Spark, and the lakehouse

Data warehouses vs lakehouses. dbt for SQL transformations, Apache Spark for distributed processing at TB scale

↗ Data Engineering Professional Certificate — Coursera (free audit)

D4–5

Streaming pipelines: Kafka and Flink for real-time ML features

Building real-time feature pipelines. Kafka for event streaming, Flink for stateful stream processing

↗ Apache Kafka official documentation (free)

D6–7

Data quality: Great Expectations, Soda, and data contracts

Automated data validation suites, data contracts between producers and consumers, automated drift alerting

↗ Great Expectations official documentation (free)

🏗 Project — Day D8: Real-Time ML Feature Pipeline 

· End-to-end pipeline: Kafka event stream to Flink processor to Feature Store

· Great Expectations automated quality checks on all features

· dbt transformation models for 10 production features

· End-to-end latency under 100ms measured and documented

Week 2: Data for LLMs: Curation, Labelling, and Synthetic Data · Days 9–24  

Build the specific data infrastructure needed for LLM pre-training and fine-tuning: web crawling, deduplication, annotation, and synthetic generation.

D9–12

LLM pre-training data: crawling, deduplication, and quality filtering

Common Crawl processing, MinHash deduplication, fastText quality filtering. How GPT-4 training data was built

↗ HuggingFace DataTrove pipeline documentation (free)

D13–16

Data labelling at scale: Label Studio, Argilla, and RLHF datasets

Annotation tooling, inter-annotator agreement measurement, building preference datasets for RLHF training

↗ Label Studio open source documentation (free)

D17–20

Synthetic data generation for ML training augmentation

LLM-generated synthetic training data, DALL-E image augmentation, privacy-preserving synthetic tabular data

↗ DeepLearning.AI synthetic data courses (free)

D21–23

Vector databases at production scale: Weaviate, Pinecone, pgvector

HNSW indexing internals, hybrid keyword plus dense search, multi-tenant vector stores, billion-vector scale

↗ Weaviate Academy learning paths (free)

🏗 Project — Day D24: LLM Training Data Curation Pipeline 

· Web crawl to filter to deduplicate pipeline with 3 quality stages

· 100K document curated training dataset

· Quality metrics report: perplexity score, diversity index, toxicity rate

· Final dataset pushed to HuggingFace Hub with dataset card

Week 3: Governance, Privacy, and Advanced Data Platform · Days 25–40  

Data governance for AI, privacy compliance engineering, observability, and building the complete enterprise AI data platform.

D25–28

Data governance: lineage, cataloguing, and access control

OpenMetadata, Apache Atlas for cataloguing, column-level data lineage tracking, PII access control policies

↗ OpenMetadata platform documentation (free)

D29–32

Privacy engineering: PII detection, masking, and synthetic replacement

Microsoft Presidio for detection, Faker for realistic synthetic PII generation, k-anonymity, GDPR Article 25

↗ Microsoft Presidio documentation (free)

D33–36

ML data observability: drift, schema drift, and freshness SLAs

Monte Carlo-style data observability, automated schema monitoring, SLA alerting for data freshness violations

↗ Evidently AI data quality monitoring (free docs)

D37–39

Cost-optimised storage: Iceberg, Delta Lake, and Parquet

Apache Iceberg table format, Z-ordering for query optimisation, compaction strategies, time travel for reproducibility

↗ Apache Iceberg official documentation (free)

🏗 Project — Day D40: Complete AI Data Platform — Full Capstone 

· End-to-end platform: ingest, validate, govern, and serve features

· Real-time and batch feature pipelines integrated with ML training

· Data catalogue with full lineage from raw source to model input

· PII detection and masking pipeline with audit log

· Cost analysis: dollars per gigabyte at production scale