Video summary

How I Learned The Cloud & Got a Job as a Cloud Engineer in 90 Days

Main summary

Key takeaways

Technology

Overview

The video shares a personal “90-day” career journey—moving from software engineering to cloud engineering—then turns into a practical playbook for learning cloud quickly, getting hired, and standing out, especially in a harder job market (as of 2026).

Key story + career pivot

  • The speaker quit a London software engineering job after feeling trapped by:
    • long commutes,
    • limited salary growth compared to inflation,
    • stagnant career prospects.
  • Their company was transitioning from on-premise infrastructure (physical servers) to cloud because:
    • traffic spikes were crashing servers,
    • idle hardware was expensive to maintain.
  • That shift created demand for cloud engineers, motivating the speaker to bet on learning cloud and landing a role within 90 days, including:
    • doubling income,
    • working from home.

Learning/selection guide: Which cloud provider to focus on

The speaker compares AWS vs Azure vs GCP, concluding AWS is the best choice for job outcomes:

  • AWS is positioned as the market leader:
    • claimed ~40% earlier,
    • still “over 30%” into 2026.
  • AWS has the most extensive free tier, enabling more hands-on practice.

Recommendation: learn one cloud deeply first. Later platforms become easier because the core concepts transfer (even if service names differ, the underlying ideas are similar).

Core fundamentals to learn first (before AWS)

The speaker frames cloud learning like building a house foundation:

  1. Networking How computers communicate over the internet; cloud infrastructure is fundamentally networks.

  2. Linux + CLI Many cloud servers use Linux; learn command-line operation.

  3. Virtualization Cloud becomes possible by splitting one physical server into multiple virtual machines.

  4. Databases Understand SQL vs NoSQL, and choose based on data and storage needs. Many cloud apps revolve around storing and retrieving data.

Certifications (entry-level AWS) + study tool mention

The video targets AWS entry-level certifications:

  • AWS Cloud Practitioner
  • AWS Solutions Architect – Associate

The speaker also mentions using free study resources (including YouTube plus their own free course).

Study Tech AI (exam prep)

They recommend Study Tech AI for AWS exam prep, describing features such as:

  • everything in one place (including note-taking/flashcards),
  • active recall and unlimited marking,
  • an AI engine that identifies knowledge gaps and focuses on weak areas,
  • a claim of faster progress (“within weeks and not months”).

Why certifications alone didn’t get interviews

After applying with certifications, the speaker reports:

  • no callbacks/interviews.

Their conclusion: hiring managers want evidence of hands-on projects solving real problems—not just exam credentials.

Project building strategy: focus on “core” AWS services

Because AWS has 200+ services, they advise against learning everything. Instead, master services commonly used across real projects:

  1. VPC (Virtual Private Cloud) Isolated networking; controls inbound/outbound access.

  2. IAM (Identity and Access Management) Permissions and keys model.

  3. EC2 (Elastic Compute Cloud) Renting virtual machine instances.

  4. S3 (Simple Storage Service) Scalable, reliable object/file storage.

The “most people get it wrong” mindset shift

A common mistake is obsessing over:

  • “Which project should I build?”

The speaker argues the bigger differentiator is the story you tell:

  • the problem you solved (business challenge),
  • why you chose specific services/architecture,
  • trade-offs and what you’d do differently next time,
  • documentation covering:
    • expected behavior at higher traffic,
    • costs at scale,
    • the security model.

Employers see many portfolios; you stand out by explaining the why, not only deploying something.

What to add to projects to stand out (2026 emphasis)

They highlight three additional pillars:

  1. Security (built-in from day one)
    • access control,
    • data protection,
    • handling unauthorized access.
  2. Infrastructure as Code (IaC)
    • prefer defining infrastructure in code (e.g., Terraform) rather than clicking the AWS console in production.
    • benefits:
      • versioned change history,
      • reproducibility across environments,
      • fewer human errors.
  3. CI/CD pipelines
    • automated testing and deployment to ship safely and quickly.
    • tools mentioned:
      • GitHub Actions
      • GitLab CI
      • AWS CodePipeline
    • goal: a professional deployment workflow vs manual updates and mistakes.

AI-native recommendation (big differentiator)

The speaker argues that if you’re starting today, you should build projects with AI awareness from day one:

  • an AI-first / AI-native mindset
  • use AWS AI services (named):
    • Amazon Bedrock
    • SageMaker
    • “Amazon (Agnition mentioned)” as a potential tool/service
  • also use productivity/learning tools:
    • GPT
    • Claude
    • GitHub Copilot

Warning: AI should amplify understanding, not replace it. Don’t outsource thinking—otherwise you may fail interviews when asked questions without AI available.

Interview + outreach tactics (LinkedIn workflow)

After building projects and documentation, the speaker recommends a LinkedIn-based process:

  • update LinkedIn,
  • post daily documenting progress,
  • leverage the recruiter behavior claim that recruiters “go to LinkedIn” for candidates (stated as ~90%).

Outreach method

  • don’t only apply—identify recruiter/hiring manager on LinkedIn and send a direct message
  • optionally send a Loom video introducing yourself

Interview formats mentioned

  • technical discussions walking through:
    • architecture,
    • decisions,
    • trade-offs,
    • scalability,
    • security,
    • cost
  • take-home assignments
  • live assessments

Theme: consistently focus on what problems the project solved and how you made decisions.

Motivation + market demand claims

The speaker acknowledges the learning journey is difficult (debugging stalls, imposter syndrome), then makes market-demand claims:

  • AWS reported a shortage of 6 million cloud professionals (as stated)
  • projected cloud market growth:
    • from ~$900B to $5.4T over the next decade

They conclude that candidates who demonstrate:

  • fundamentals,
  • real project proof,
  • engineering thinking,
  • and AI-enabled capability

are well-positioned.

Main speakers / sources

  • Main speaker: the video’s narrator (the creator / “I” throughout), who describes their own learning journey and mentions their “cloud engineering academy.”
  • Referenced sources/tools:
    • AWS (market share/free tier and AI services),
    • Study Tech AI (exam prep),
    • Terraform (IaC concept/tool),
    • GitHub Actions / GitLab CI / AWS CodePipeline (CI/CD),
    • LinkedIn (recruiter outreach),
    • Amazon Bedrock / SageMaker (AI services),
    • GPT / Claude / GitHub Copilot (AI tools).

Original video