Interviews/Infrastructure & Reliability/Cloud Engineer (AWS, Azure, GCP)

Hire Cloud Engineers
who actually design infra that survives the bill

With EasyEnv we build a fit test for the role. Use it to cut the noise at the top of the funnel, or to go deep on the candidates who matter.

You can even evaluate how this person works with AI. So you hire the engineer who can ship, not the one who looks good in a slide.

AWSGCPAzureTerraform · PulumiLive or take-home

Request a Demo Create a Free Account

Real Linux VM

Not a sandbox

systemd, kernel, full toolchain

Browser-based

Zero install for candidates

One link and they are in

Auto-graded

Pass / fail checks per challenge

Plus the full session replay

A great cloud engineer is fluent across at least two clouds and humble about the third. They know which knobs matter, which cost money, and which page someone on a Saturday. EasyEnv gives the candidate a sandbox account, real CLIs and a scenario that needs more than a console click.

Inside a live interview

What you see when they work.

Sandbox cloud account, real CLI, every keystroke recorded.

billing-api

AMS

Billing-api

src

billing.py

auth.py

models.py

tests

test_billing.pyM

test_auth.py

pyproject.toml

README.md

Pybilling.py

from decimal import Decimal

def calculate_total(items: list[dict]) -> Decimal:

# Sum line totals using Decimal for currency precision

total = Decimal("0")

for item in items:

total += Decimal(str(item["price"])) * item["qty"]

return total

candidate@cloud-workspace:~

alice@dev:~/billing-api$

● mainPython 3.12

Claude · ready

Claude

You

Refactor calculate_total to use Decimal for currency math, and add a test.

Claude

Ask Claude...

Mike

Sam

How it works

Three steps. Zero infra to maintain.

Pick the fit test

Use a ready-made challenge or ask us to build one for your stack. Live, take-home, or both.

Send one link

The candidate clicks. A full Linux workspace boots in their browser. No installs, no VPN, no zoom-share.

Review at 2x

Replay the session, read the auto-graded checks, and decide. Most reviews take under five minutes.

The problem

Why hiring a Cloud Engineer (AWS, Azure, GCP) is hard.

Cloud breadth is uneven. Strong AWS does not mean strong GCP, and CVs blur the difference.

IAM is the second-hardest part of any cloud. The first is cost. Neither shows in a whiteboard.

Service catalogs change every quarter. You want fundamentals, not memorised SKUs.

Multi-cloud is a buzzword. The interview has to draw out a real opinion.

The fit test

What we measure for a Cloud Engineer (AWS, Azure, GCP).

Every area runs inside a real production-like workspace. The candidate works in a browser, the session records itself.

CLI and SDK fluency

Real aws, gcloud or az commands against a sandbox account. They use the CLI, not a console.

IAM and least privilege

A policy that "works" but grants too much. They tighten it without breaking the workload.

Cost reasoning

A bill with one weird line item. They isolate the cause from Cost Explorer or BigQuery billing export.

Multi-region design

Live conversation about active-active versus active-passive, anchored to a real VPC.

Cloud-native CI/CD

A CodePipeline, Cloud Build or Azure DevOps pipeline that should ship to two regions but only ships to one.

AI collaboration

Allow AI for unfamiliar service syntax and grade whether they validate the call before they run it on the account.

The stack we put in front of them.

Real tools, pre-installed, accessible from the workspace terminal.

AWSGCPAzureTerraformPulumiCDKCrossplaneaws-cligcloudazIAMCloudWatchCloud LoggingCost ExplorerBigQueryEKSGKEAKSKubernetes

Sample challenges

What a Cloud Engineer (AWS, Azure, GCP) interview looks like.

Pick from our library or ask us to build a challenge for your stack. Each one is a real workspace, not a snippet.

Challenge 01

The policy that does too much

Scenario

A workload role grants s3:*. The candidate has the role, the calling service and CloudTrail.

What you learn

Whether they read CloudTrail to scope down without breaking the job.

In the workspace

$ ls challenges/

workspace, runbook.md, README.md

candidate has:

· a real Linux box in the browser
· kubectl, docker, terraform, jq, yq
· cluster, repo and cloud creds pre-wired
· auto-checks running in the background

interviewer sees:

· every keystroke + every command
· terminal + screen recording
· auto-graded pass/fail per check

AI in the loop

Score the engineer, and how they work with AI.

AI is brilliant at remembering cloud CLI syntax and dangerous at inventing it. EasyEnv lets you allow AI in the cloud interview and replay the prompts, so you see whether the candidate read what came back before they ran it against a real account.

Allow or block AI per question, not per interview

Full prompt and response history replayed in the review

Score "verifies AI output" as a separate signal

Ready to hire a Cloud Engineer (AWS, Azure, GCP) who ships?

Set up your first interview in under an hour. We will help you build a fit test for this role on your stack.

Request a Demo Create a Free Account

Role FAQ

Hiring a Cloud Engineer (AWS, Azure, GCP), answered.

Yes. We can pre-load sandbox accounts on any subset of clouds. Candidates switch between them in the same workspace.

Yes, scoped to a sandbox account and time-boxed to the interview, with full audit log.

No. The workspace runs in their browser. Terminal, editor, ports and dashboards are all served from the EasyEnv workspace, so candidate setup is zero.

Each challenge has automated checks (did the service come back, did the test pass, did the right log message appear) plus a recording your team can replay. Most reviews take under five minutes.

Yes. AI access is a per-interview setting. Block it for fundamentals, then allow it later in the loop to assess AI collaboration separately.

Interviews/Infrastructure & Reliability/Cloud Engineer (AWS, Azure, GCP)

Hire Cloud Engineers
who actually design infra that survives the bill

With EasyEnv we build a fit test for the role. Use it to cut the noise at the top of the funnel, or to go deep on the candidates who matter.

You can even evaluate how this person works with AI. So you hire the engineer who can ship, not the one who looks good in a slide.

AWSGCPAzureTerraform · PulumiLive or take-home

Request a Demo Create a Free Account

Real Linux VM

Not a sandbox

systemd, kernel, full toolchain

Browser-based

Zero install for candidates

One link and they are in

Auto-graded

Pass / fail checks per challenge

Plus the full session replay

Inside a live interview

What you see when they work.

Sandbox cloud account, real CLI, every keystroke recorded.

billing-api

AMS

Billing-api

src

billing.py

auth.py

models.py

tests

test_billing.pyM

test_auth.py

pyproject.toml

README.md

Pybilling.py

from decimal import Decimal

def calculate_total(items: list[dict]) -> Decimal:

# Sum line totals using Decimal for currency precision

total = Decimal("0")

for item in items:

total += Decimal(str(item["price"])) * item["qty"]

return total

candidate@cloud-workspace:~

alice@dev:~/billing-api$

● mainPython 3.12

Claude · ready

Claude

You

Refactor calculate_total to use Decimal for currency math, and add a test.

Claude

Ask Claude...

Mike

Sam

How it works

Three steps. Zero infra to maintain.

Pick the fit test

Use a ready-made challenge or ask us to build one for your stack. Live, take-home, or both.

Send one link

The candidate clicks. A full Linux workspace boots in their browser. No installs, no VPN, no zoom-share.

Review at 2x

Replay the session, read the auto-graded checks, and decide. Most reviews take under five minutes.

The problem

Why hiring a Cloud Engineer (AWS, Azure, GCP) is hard.

Cloud breadth is uneven. Strong AWS does not mean strong GCP, and CVs blur the difference.

IAM is the second-hardest part of any cloud. The first is cost. Neither shows in a whiteboard.

Service catalogs change every quarter. You want fundamentals, not memorised SKUs.

Multi-cloud is a buzzword. The interview has to draw out a real opinion.

The fit test

What we measure for a Cloud Engineer (AWS, Azure, GCP).

Every area runs inside a real production-like workspace. The candidate works in a browser, the session records itself.

CLI and SDK fluency

Real aws, gcloud or az commands against a sandbox account. They use the CLI, not a console.

IAM and least privilege

A policy that "works" but grants too much. They tighten it without breaking the workload.

Cost reasoning

A bill with one weird line item. They isolate the cause from Cost Explorer or BigQuery billing export.

Multi-region design

Live conversation about active-active versus active-passive, anchored to a real VPC.

Cloud-native CI/CD

A CodePipeline, Cloud Build or Azure DevOps pipeline that should ship to two regions but only ships to one.

AI collaboration

Allow AI for unfamiliar service syntax and grade whether they validate the call before they run it on the account.

The stack we put in front of them.

Real tools, pre-installed, accessible from the workspace terminal.

AWSGCPAzureTerraformPulumiCDKCrossplaneaws-cligcloudazIAMCloudWatchCloud LoggingCost ExplorerBigQueryEKSGKEAKSKubernetes

Sample challenges

What a Cloud Engineer (AWS, Azure, GCP) interview looks like.

Pick from our library or ask us to build a challenge for your stack. Each one is a real workspace, not a snippet.

Challenge 01

The policy that does too much

Scenario

A workload role grants s3:*. The candidate has the role, the calling service and CloudTrail.

What you learn

Whether they read CloudTrail to scope down without breaking the job.

In the workspace

$ ls challenges/

workspace, runbook.md, README.md

candidate has:

· a real Linux box in the browser
· kubectl, docker, terraform, jq, yq
· cluster, repo and cloud creds pre-wired
· auto-checks running in the background

interviewer sees:

· every keystroke + every command
· terminal + screen recording
· auto-graded pass/fail per check

AI in the loop

Score the engineer, and how they work with AI.

Allow or block AI per question, not per interview

Full prompt and response history replayed in the review

Score "verifies AI output" as a separate signal

Ready to hire a Cloud Engineer (AWS, Azure, GCP) who ships?

Set up your first interview in under an hour. We will help you build a fit test for this role on your stack.

Request a Demo Create a Free Account

Role FAQ

Hiring a Cloud Engineer (AWS, Azure, GCP), answered.

Yes. We can pre-load sandbox accounts on any subset of clouds. Candidates switch between them in the same workspace.

Yes, scoped to a sandbox account and time-boxed to the interview, with full audit log.

No. The workspace runs in their browser. Terminal, editor, ports and dashboards are all served from the EasyEnv workspace, so candidate setup is zero.

Each challenge has automated checks (did the service come back, did the test pass, did the right log message appear) plus a recording your team can replay. Most reviews take under five minutes.

Yes. AI access is a per-interview setting. Block it for fundamentals, then allow it later in the loop to assess AI collaboration separately.

How to Hire a Cloud Engineer | AWS · GCP · Azure | EasyEnv | EasyEnv

Hire Cloud Engineerswho actually design infra that survives the bill

What you see when they work.

Three steps. Zero infra to maintain.

Pick the fit test

Send one link

Review at 2x

Why hiring a Cloud Engineer (AWS, Azure, GCP) is hard.

What we measure for a Cloud Engineer (AWS, Azure, GCP).

CLI and SDK fluency

IAM and least privilege

Cost reasoning

Multi-region design

Cloud-native CI/CD

AI collaboration

The stack we put in front of them.

What a Cloud Engineer (AWS, Azure, GCP) interview looks like.

The policy that does too much

Score the engineer, and how they work with AI.

Ready to hire a Cloud Engineer (AWS, Azure, GCP) who ships?

Hiring a Cloud Engineer (AWS, Azure, GCP), answered.

Hire Cloud Engineerswho actually design infra that survives the bill

What you see when they work.

Three steps. Zero infra to maintain.

Pick the fit test

Send one link

Review at 2x

Why hiring a Cloud Engineer (AWS, Azure, GCP) is hard.

What we measure for a Cloud Engineer (AWS, Azure, GCP).

CLI and SDK fluency

IAM and least privilege

Cost reasoning

Multi-region design

Cloud-native CI/CD

AI collaboration

The stack we put in front of them.

What a Cloud Engineer (AWS, Azure, GCP) interview looks like.

The policy that does too much

Score the engineer, and how they work with AI.

Ready to hire a Cloud Engineer (AWS, Azure, GCP) who ships?

Hiring a Cloud Engineer (AWS, Azure, GCP), answered.

Hire Cloud Engineers
who actually design infra that survives the bill

Hire Cloud Engineers
who actually design infra that survives the bill