AWS Engineer → AI Engineer

AWS Engineer to AI Engineer: Cloud Infrastructure to AI Systems

Transition from AWS cloud engineering to AI engineering by leveraging your deep infrastructure expertise. Your experience with scalable architectures, serverless computing, and AWS services provides an exceptional foundation for building production AI systems. This path focuses on AWS-native AI services. Amazon Bedrock for foundation models, SageMaker for custom ML workflows, and serverless patterns for AI inference. You already understand the deployment and scaling challenges that trip up most AI engineers; now you'll learn to build the AI systems that run on that infrastructure. Your IAM, networking, and cost optimization skills become critical differentiators when deploying AI at scale. Timeline: 4-6 months to production-ready AI engineering skills.

4-6 months

Difficulty: Intermediate

Prerequisites

AWS Lambda and serverless architecture experience
S3 storage patterns and data management
IAM roles, policies, and security best practices
EC2/ECS/EKS container orchestration
CloudFormation or CDK infrastructure as code
API Gateway and networking fundamentals

Your Learning Path

AI Fundamentals for Cloud Engineers

2-3 weeks

Skills You'll Build

How LLMs work (tokens, context windows, inference)Understanding embeddings and vector representationsPrompt engineering fundamentalsAI model types (foundation models, fine-tuned, custom)Cost structures for AI inference vs traditional compute

Amazon Bedrock & Foundation Models

3-4 weeks

Skills You'll Build

Bedrock console and API fundamentalsWorking with Claude, Titan, and other Bedrock modelsBedrock guardrails and content filteringModel selection and cost optimizationBedrock agents and knowledge bases

Serverless AI Architecture Patterns

3-4 weeks

Skills You'll Build

Lambda functions for AI inferenceAPI Gateway integration for AI endpointsStep Functions for AI workflows and orchestrationEventBridge for async AI processingManaging Lambda cold starts with AI workloads

RAG Systems on AWS

3-4 weeks

Skills You'll Build

Amazon OpenSearch for vector searchBedrock Knowledge Bases configurationS3 document ingestion pipelinesEmbedding generation with Titan or external modelsHybrid search patterns on AWS

SageMaker for Custom AI Workflows

3-4 weeks

Skills You'll Build

SageMaker Studio and notebook environmentsModel training and fine-tuning basicsSageMaker endpoints for custom model hostingSageMaker Pipelines for ML workflowsWhen to use Bedrock vs SageMaker

Production AI Infrastructure

2-3 weeks

Skills You'll Build

AI model monitoring and observabilityCost optimization for AI workloadsMulti-region AI deploymentsSecurity and compliance for AI systemsInfrastructure as code for AI stacks

Portfolio & Career Transition

3-4 weeks

Skills You'll Build

End-to-end AI project on AWSDemonstrating cloud + AI expertiseTechnical interview preparationSystem design for AI applicationsPositioning as AI infrastructure specialist

AWS Engineer to AI Engineer: Cloud Infrastructure to AI Systems

Prerequisites

Your Learning Path

AI Fundamentals for Cloud Engineers

🎁 The AI Engineer Starter Kit

Amazon Bedrock & Foundation Models

Serverless AI Architecture Patterns

RAG Systems on AWS

SageMaker for Custom AI Workflows

Production AI Infrastructure

Portfolio & Career Transition

🎁 The AI Engineer Starter Kit

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