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Hi Cloud Builders,

Over the last three weeks, we’ve explored what it means to become an AI-Ready Cloud Engineer, how the role is evolving, and how AWS and Azure each provide powerful paths into this new era of intelligent infrastructure. Check out the 3 previous parts, here, here, and here!

Today, in our final part of the series, we’re shifting from concepts → to certifications → to real projects you can build right now.

Because at the end of the day, nothing accelerates your career faster than showing, not telling, that you can build intelligent cloud systems.

This edition gives you two weekend-ready projects:

• One for AWS

• One for Azure

Both are resume-ready, GitHub-friendly, and interview-boosting. And more importantly, both reflect the exact architectures companies have been rolling out internally in 2025 and beyond.

Let’s build.

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Why Projects Matter More Than Ever

Cloud certification is still crucial—it gives you the structure, vocabulary, and foundational knowledge you’ll rely on daily. But in the age of fast-moving AI adoption, companies want something more:

Proof you can integrate AI into real cloud systems.

They want to see:

• Can you connect an AI model to an event-driven workflow?

• Can you secure it?

• Can you scale it?

• Can you combine cloud fundamentals with intelligent behavior that adds value?

These weekend projects check every one of those boxes.

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Project 1 (AWS): The Intelligent Resume Screener

Difficulty: Beginner → Intermediate
Time: 4–6 hours
Cloud: AWS
Skills You’ll Demonstrate:
Event-driven design, Lambda + Bedrock integration, S3 automation, DynamoDB storage, IAM fundamentals.

What You’re Building

A system that automatically analyzes resumes dropped into an S3 bucket and returns:

• Candidate name

• Top skills

• Years of experience

• A short AI-generated summary

This mimics a real workflow used in HR tech, talent acquisition, and internal automation platforms.

Architecture Overview

Here’s the flow:

1. A resume (PDF or DOCX) is uploaded to S3.

2. S3 triggers an EventBridge event.

3. EventBridge calls a Lambda function.

4. Lambda extracts text, sends it to Amazon Bedrock (Claude).

5. Lambda stores the structured results (JSON) in DynamoDB.

6. (Optional) Another Lambda sends a notification to Slack or email with the summary.

This is a real automation pattern showing you can integrate AI into cloud-native workflows.

Steps to Build It

Step 1: Create your S3 buckets

• resume-input

• resume-output (optional)

Enable EventBridge notifications for all object creation events.

Step 2: Create your DynamoDB table
Partition key: candidateId
Attributes: name, skills, experienceYears, summary, timestamp.

Step 3: Write your Lambda function
The function should:

• Extract text from PDF (use Amazon Textract or a simple parser for MVP)

• Send text to Bedrock with a system prompt:
  “Extract the candidate’s name, top 3 skills, and years of experience. Then generate a 3–4 sentence summary.”

• Store the results in DynamoDB

Step 4: Assign IAM permissions

• S3 read access

• DynamoDB write access

• Bedrock invocation access

Step 5: Trigger through EventBridge
Map the S3 event → Lambda function.

Step 6: Test with a real resume
Drop a file into the bucket → watch the pipeline run → confirm DynamoDB output.

Why This Project Works

This project demonstrates:

• You understand how AWS event-driven systems work

• You can orchestrate Lambda + Bedrock

• You can structure data and store results cleanly

• You can build a real-world automation pipeline

This is the exact type of architecture used in internal teams everywhere—from HR to operations to customer service.

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Project 2 (Azure): The Internal Knowledge Search Bot

Difficulty: Intermediate
Time: 6–8 hours
Cloud: Azure
Skills You’ll Demonstrate:
Azure OpenAI, Cognitive Search, RAG architecture, private endpoints, Functions, storage pipelines.

What You’re Building

A chat-like tool that allows users to ask questions based on a set of internal documents. The system uses Retrieval-Augmented Generation (RAG) to give grounded, accurate answers using your uploaded content.

This is the #1 most in-demand Azure AI pattern right now.

Architecture Overview

1. Documents are uploaded to Azure Blob Storage.

2. Cognitive Search indexes them (PDFs, text, Office files).

3. A user query is sent to an Azure Function.

4. The Function retrieves top matches from Cognitive Search.

5. It passes both query + search results to Azure OpenAI (GPT-4o).

6. The model generates a grounded, context-aware answer.

7. The response is returned via an API or chat app.

Steps to Build It

Step 1: Create a Storage account
Upload a handful of PDFs or Markdown files—company docs, help guides, anything text-based.

Step 2: Create an Azure Cognitive Search instance
Enable vector search if available in your region.
Index fields like:

• content

• title

• metadata

• embedding vector (optional RAG optimization)

Step 3: Create an Azure OpenAI resource
Deploy GPT-4o or GPT-4 Turbo depending on availability.

Step 4: Build an Azure Function
This function will:

• Accept a user query

• Run a search against Cognitive Search

• Extract relevant chunks

• Send everything to Azure OpenAI with a system prompt:
  “Answer the user’s question using ONLY the following context. If the context does not contain the answer, say you do not know.”

• Return the response as JSON

Step 5: Add security
Use:

• Managed Identity

• Private endpoints

• Role assignments (RBAC)

Step 6: Create a simple front-end (optional)
This can be a minimal HTML/JS page hosted in Blob Static Website Hosting or a basic React front-end.

Why This Project Works

This project proves you can build RAG — the architecture every enterprise is experimenting with. You demonstrate:

• Document ingestion

• Indexing

• AI reasoning grounded in real data

• Azure’s identity/security model

• Production-ready patterns

This is the kind of project that immediately stands out in a portfolio.

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How These Projects Accelerate Your Cloud Career

You now have two real-world builds that reflect exactly what companies are deploying internally.

These aren’t toy examples.
They’re systems that:

• Automate workflows

• Interpret documents

• Answer questions

• Save teams hours

• Scale securely

• Integrate cleanly with cloud providers

This is how you set yourself apart as an AI-Ready Cloud Engineer.

A Quick Note for Our Newsletter Subscribers

If you want structured support as you continue your journey into AWS, Azure, and AI certifications, LearnCloudAcademy.com is the best place to start.

Everything inside the platform is designed around the exact roadmap we’ve covered in this series:

• Azure + AWS fundamentals

• Administrator and Architect content

• AI-focused pathways

• Hands-on style practice questions

• Certification-aligned explanations

And because you’re a subscriber, you get 25% off any plan — even Lifetime.

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We would love to see you inside.

Next week, we’ll have a new certification spotlight you don’t want to miss — but for now, congratulations on completing the AI-Ready Cloud Engineer journey.

Keep learning and building.

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