Applied AI Engineer

You've built agents in production with real users.

Not integrated a framework.

Not worked on a team that had one.

Built them — agents with real autonomy and internal loops, where the model uses tools and decides what to do next without a human in the middle, and you kept them running once real users showed up.

RAG with a wrapper doesn't count.

Neither does a set of MCP tools point at an API.

You've run evals and monitored agents after launch.

Agents are non-deterministic, so normal tests don't work on them.

You've owned an eval framework — golden datasets, regression suites, offline and online scoring — not a folder of one-off scripts.

And you've watched agents in production, not just in dev.

Python, plus an ML or data science background.

Python is your daily language and you're solid on the backend, though you don't need to be a distributed systems specialist.

You understand models well enough to reason about how they behave — you're not an application engineer calling someone else's API.

You work from a problem, not a spec.

Handed an ambiguous problem statement, you design the experiment, build the smallest version to test it, and take what works into production.

You use AI tools every day.

Claude or its equivalents are part of how you write code and do research, not something you tried once.

This is backend and model layer work, by the way — no frontend.

You'd rather ship than polish.

Most of this work needs a good answer quickly, not a perfect one eventually.

You can tell which problems are the exception and deserve real depth — and you'll say no to the version that demos well and falls apart in production.

Nice to have: statistics and hypothesis testing, applied rather than theoretical.

Building and maintaining MCP servers.

in the data and cloud space — Snowflake, Databricks, dbt, Airflow.

Your AI work is retrieval with a wrapper, or MCP tools pointed at an API — nothing that decides and acts on its own

Your LLM experience is prototypes, notebooks, and demos that never carried production traffic

You need a fully specified problem before you start, or you're uncomfortable with the ambiguity of a category being invented in real time

We created the data observability category and we're doing it again with agent observability — you'll build where the market is forming, not where it's settled

Series D, $236M raised, backed by Accel, Redpoint, Notable Capital, ICONIQ Growth, and Salesforce Ventures

Customers include HubSpot, Fox, Nasdaq, Toast, and Mercado Libre — your work ships to enterprises with real stakes

Snowflake Partner of the Year and a verified connector in Anthropic's Claude AI directory

Remote-first by design since day one, and recognized as a Best Workplace for it

Competitive compensation, equity, and a remote-first environment.

#LI-REMOTE

#BI-REMOTE

Equality is a core tenet of Monte Carlo's culture.

We are committed to building an inclusive global team that represents a variety of backgrounds, perspectives, beliefs, and experiences.

Monte Carlo is an equal-opportunity employer.

We celebrate diversity and are committed to creating an inclusive environment for all employees.

We are proud to be recognized for our world-class employee experience:

Monte Carlo Named 2025 Databricks Data Governance Partner of the Year

We were recently recognized as the #1 Data Observability Platform by G2 for the 4th consecutive quarter.

See our G2 reviews here!

Monte Carlo Named to G2's Best Software Products of 2026

Monte Carlo was featured on Database Trends and Applications (DBTA’s) Trend-Setting Products for 2025!

We are super proud to be named the 2026 Best Place to Work by Built In!

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