Interviews/Data & AI/AI/ML Ops Engineer

Hire AI/ML Ops Engineers
who actually ship models that stay correct

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.

MLflow · Weights & BiasesRay · KServeFeature storesDrift checksLive 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

An MLOps engineer is the difference between a model that ships and a model that haunts the team. The good ones think about versioning, drift and rollback before they think about latency. EasyEnv hands the candidate a workspace with a model server, a registry and a slow accuracy slide.

Inside a live interview

What you see when they work.

Real model server, real registry, 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@mlops-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 AI/ML Ops Engineer is hard.

MLOps is half infra, half data. Few candidates do both well.

Drift is silent. The interview has to put it in front of them.

Tools change yearly. MLflow, BentoML, KServe, Ray Serve, each with its own habits.

Rollback for models is harder than for binaries. Judgement matters more than tooling.

The fit test

What we measure for a AI/ML Ops Engineer.

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

Model deployment

A model in a registry, a serving target waiting. They wire it up with health checks and limits.

Drift detection

A live dashboard where inputs have shifted in the last week. They explain and react.

Rollback strategy

A new model behind a feature flag. They design the criteria that triggers a rollback.

Feature store fluency

A feature pipeline with stale offline-online parity. They reconcile without re-training.

Live triage

A model returning wrong predictions on one segment. They isolate without breaking the rest.

AI collaboration

Allow AI for boilerplate and grade whether they sanity-check the serving config against load.

The stack we put in front of them.

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

MLflowWeights & BiasesKServeBentoMLRay ServeTorchServeTritonFeastTectonAirflowdbtArgo WorkflowsKubernetesAWSGCP

Sample challenges

What a AI/ML Ops Engineer 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 shift no one logged

Scenario

A model loses 4% accuracy on European traffic since Monday. The candidate has logs, features and the deploy history.

What you learn

Whether they look at input distribution before they retrain anything.

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.

MLOps work is full of glue scripts where AI is a fast typist. EasyEnv records every prompt and the serving config it touched, so you see whether the candidate verified the model spec before letting it reach production.

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 AI/ML Ops Engineer 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 AI/ML Ops Engineer, answered.

On enterprise plans, we can connect a read-only mirror of your MLflow, W&B or in-house registry.

Yes. The workspace can run Feast or a custom store with sample offline and online tables ready for a join test.

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/Data & AI/AI/ML Ops Engineer

Hire AI/ML Ops Engineers
who actually ship models that stay correct

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.

MLflow · Weights & BiasesRay · KServeFeature storesDrift checksLive 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.

Real model server, real registry, 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@mlops-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 AI/ML Ops Engineer is hard.

MLOps is half infra, half data. Few candidates do both well.

Drift is silent. The interview has to put it in front of them.

Tools change yearly. MLflow, BentoML, KServe, Ray Serve, each with its own habits.

Rollback for models is harder than for binaries. Judgement matters more than tooling.

The fit test

What we measure for a AI/ML Ops Engineer.

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

Model deployment

A model in a registry, a serving target waiting. They wire it up with health checks and limits.

Drift detection

A live dashboard where inputs have shifted in the last week. They explain and react.

Rollback strategy

A new model behind a feature flag. They design the criteria that triggers a rollback.

Feature store fluency

A feature pipeline with stale offline-online parity. They reconcile without re-training.

Live triage

A model returning wrong predictions on one segment. They isolate without breaking the rest.

AI collaboration

Allow AI for boilerplate and grade whether they sanity-check the serving config against load.

The stack we put in front of them.

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

MLflowWeights & BiasesKServeBentoMLRay ServeTorchServeTritonFeastTectonAirflowdbtArgo WorkflowsKubernetesAWSGCP

Sample challenges

What a AI/ML Ops Engineer 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 shift no one logged

Scenario

A model loses 4% accuracy on European traffic since Monday. The candidate has logs, features and the deploy history.

What you learn

Whether they look at input distribution before they retrain anything.

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 AI/ML Ops Engineer 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 AI/ML Ops Engineer, answered.

On enterprise plans, we can connect a read-only mirror of your MLflow, W&B or in-house registry.

Yes. The workspace can run Feast or a custom store with sample offline and online tables ready for a join test.

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 an AI/ML Ops Engineer | EasyEnv | EasyEnv

Hire AI/ML Ops Engineerswho actually ship models that stay correct

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 AI/ML Ops Engineer is hard.

What we measure for a AI/ML Ops Engineer.

Model deployment

Drift detection

Rollback strategy

Feature store fluency

Live triage

AI collaboration

The stack we put in front of them.

What a AI/ML Ops Engineer interview looks like.

The shift no one logged

Score the engineer, and how they work with AI.

Ready to hire a AI/ML Ops Engineer who ships?

Hiring a AI/ML Ops Engineer, answered.

Hire AI/ML Ops Engineerswho actually ship models that stay correct

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 AI/ML Ops Engineer is hard.

What we measure for a AI/ML Ops Engineer.

Model deployment

Drift detection

Rollback strategy

Feature store fluency

Live triage

AI collaboration

The stack we put in front of them.

What a AI/ML Ops Engineer interview looks like.

The shift no one logged

Score the engineer, and how they work with AI.

Ready to hire a AI/ML Ops Engineer who ships?

Hiring a AI/ML Ops Engineer, answered.

Hire AI/ML Ops Engineers
who actually ship models that stay correct

Hire AI/ML Ops Engineers
who actually ship models that stay correct