Data Engineer → AI Engineer

Data Engineer to AI Engineer: From Pipelines to ML Pipelines

Data engineers have one of the smoothest transitions into AI engineering. Your expertise in building robust data pipelines, managing large-scale data processing, and ensuring data quality translates directly to ML infrastructure. The same skills you use to orchestrate ETL workflows apply to feature pipelines and model serving. Your experience with tools like Airflow, Spark, and cloud data services maps closely to MLOps platforms. This path focuses on extending your data infrastructure skills to encompass the full ML lifecycle, from feature stores and training pipelines to inference endpoints and model monitoring. You already understand data at scale; now you'll learn to make that data power intelligent systems. The key transition is shifting from data transformation for analytics to data transformation for machine learning, including embedding generation, vector storage, and retrieval-augmented generation pipelines. Timeline: 3-5 months.

3-5 months

Difficulty: Intermediate

Prerequisites

Strong SQL and database optimization skills
Python proficiency for data processing
ETL/ELT pipeline development experience
Workflow orchestration (Airflow, Dagster, Prefect)
Cloud data services (AWS, GCP, or Azure)
Data modeling and warehousing concepts

Your Learning Path

AI/ML Fundamentals for Data Engineers

2-3 weeks

Skills You'll Build

How LLMs and ML models work conceptuallyUnderstanding training vs inference pipelinesEmbeddings and vector representationsData requirements for ML systemsPrompt engineering fundamentals

Feature Engineering & Feature Stores

3-4 weeks

Skills You'll Build

Feature engineering for ML modelsFeature store architecture (Feast, Tecton)Online vs offline feature servingFeature pipelines and versioningData validation for ML features

ML Pipeline Development

3-4 weeks

Skills You'll Build

MLflow for experiment trackingTraining pipeline orchestrationModel versioning and registryKubeflow and cloud ML platformsCI/CD for ML models

RAG Data Pipelines & Vector Infrastructure

3-4 weeks

Skills You'll Build

Document ingestion pipelinesText chunking and embedding generationVector database operations at scaleRetrieval pipeline optimizationHybrid search implementations

MLOps & Production AI Systems

3-4 weeks

Skills You'll Build

Model serving infrastructureInference optimization and batchingModel monitoring and observabilityA/B testing for ML systemsCost optimization for AI workloads

Portfolio & Career Transition

2-3 weeks

Skills You'll Build

End-to-end ML pipeline projectDemonstrating data + AI expertiseTechnical interview preparationSystem design for AI infrastructurePositioning data engineering experience

Data Engineer to AI Engineer: From Pipelines to ML Pipelines

Prerequisites

Your Learning Path

AI/ML Fundamentals for Data Engineers

🎁 The AI Engineer Starter Kit

Feature Engineering & Feature Stores

ML Pipeline Development

RAG Data Pipelines & Vector Infrastructure

MLOps & Production AI Systems

Portfolio & Career Transition

🎁 The AI Engineer Starter Kit

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