Interviews/AI & Machine Learning/AI Engineer

Hire AI Engineers
who actually ship LLM features that survive production

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.

LLM appsRAG · evalsLangChain · LlamaIndexOpenAI · AnthropicLive 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

AI engineers wire models into product. The good ones treat prompts and retrieval as code, write evals before they ship, and know which failure modes the model owns versus the system. EasyEnv hands the candidate a real RAG pipeline with a real regression and watches what they reach for.

Inside a live interview

What you see when they work.

Real RAG pipeline, real traces, 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@ai-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 Engineer is hard.

Everyone has a "GenAI project" on the CV. Few have shipped something that survived contact with users.

Eval discipline is rare. The interview has to surface whether they measure or just vibe-check.

Prompts feel like text and behave like code. You need signal on whether they version, diff and test them.

Token costs and latency are real. Architecture decisions hide in the bill.

The fit test

What we measure for a AI Engineer.

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

RAG quality

A retrieval pipeline that returns the wrong chunks. They diagnose chunking, embedding model, hybrid search and rerank in order.

Eval harness

A pass-rate metric that lies. They add the eval that would have caught the regression.

Tool use and agents

An agent loop that loops. They cap, instrument, and fix without removing the tool.

Prompt injection

A prompt with a sneaky user input. They harden without breaking the happy path.

Observability

A prompt that regressed. They find the diff between the last good and the broken run.

Cost and latency

A feature priced into oblivion. They cut tokens or hops without losing quality.

The stack we put in front of them.

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

PythonTypeScriptOpenAI APIAnthropic APILangChainLlamaIndexDSPypgvectorPineconeWeaviateQdrantChromaRagaspromptfooLangfusePhoenix

Sample challenges

What a AI 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 chunk that never shows up

Scenario

A RAG pipeline returns near-misses for a known question. Index, embeddings and retriever are all open.

What you learn

Whether they fix chunking before they swap models.

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 engineering is the one role where AI in the interview is the role. EasyEnv records every prompt and tool call so you see whether the candidate runs an eval against the LLM-suggested change, or merges on the first green run.

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 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 Engineer, answered.

Yes. The workspace can be wired to your OpenAI, Anthropic or self-hosted model endpoint with a scoped key for the interview.

Yes. LangChain, LlamaIndex, DSPy and bespoke agent loops can all be pre-loaded with a real tool surface.

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/AI & Machine Learning/AI Engineer

Hire AI Engineers
who actually ship LLM features that survive production

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.

LLM appsRAG · evalsLangChain · LlamaIndexOpenAI · AnthropicLive 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 RAG pipeline, real traces, 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@ai-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 Engineer is hard.

Everyone has a "GenAI project" on the CV. Few have shipped something that survived contact with users.

Eval discipline is rare. The interview has to surface whether they measure or just vibe-check.

Prompts feel like text and behave like code. You need signal on whether they version, diff and test them.

Token costs and latency are real. Architecture decisions hide in the bill.

The fit test

What we measure for a AI Engineer.

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

RAG quality

A retrieval pipeline that returns the wrong chunks. They diagnose chunking, embedding model, hybrid search and rerank in order.

Eval harness

A pass-rate metric that lies. They add the eval that would have caught the regression.

Tool use and agents

An agent loop that loops. They cap, instrument, and fix without removing the tool.

Prompt injection

A prompt with a sneaky user input. They harden without breaking the happy path.

Observability

A prompt that regressed. They find the diff between the last good and the broken run.

Cost and latency

A feature priced into oblivion. They cut tokens or hops without losing quality.

The stack we put in front of them.

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

PythonTypeScriptOpenAI APIAnthropic APILangChainLlamaIndexDSPypgvectorPineconeWeaviateQdrantChromaRagaspromptfooLangfusePhoenix

Sample challenges

What a AI 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 chunk that never shows up

Scenario

A RAG pipeline returns near-misses for a known question. Index, embeddings and retriever are all open.

What you learn

Whether they fix chunking before they swap models.

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 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 Engineer, answered.

Yes. The workspace can be wired to your OpenAI, Anthropic or self-hosted model endpoint with a scoped key for the interview.

Yes. LangChain, LlamaIndex, DSPy and bespoke agent loops can all be pre-loaded with a real tool surface.

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 Engineer | LLM App Interviews | EasyEnv | EasyEnv

Hire AI Engineerswho actually ship LLM features that survive production

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 Engineer is hard.

What we measure for a AI Engineer.

RAG quality

Eval harness

Tool use and agents

Prompt injection

Observability

Cost and latency

The stack we put in front of them.

What a AI Engineer interview looks like.

The chunk that never shows up

Score the engineer, and how they work with AI.

Ready to hire a AI Engineer who ships?

Hiring a AI Engineer, answered.

Hire AI Engineerswho actually ship LLM features that survive production

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 Engineer is hard.

What we measure for a AI Engineer.

RAG quality

Eval harness

Tool use and agents

Prompt injection

Observability

Cost and latency

The stack we put in front of them.

What a AI Engineer interview looks like.

The chunk that never shows up

Score the engineer, and how they work with AI.

Ready to hire a AI Engineer who ships?

Hiring a AI Engineer, answered.

Hire AI Engineers
who actually ship LLM features that survive production

Hire AI Engineers
who actually ship LLM features that survive production