Applied Forward Deployed Engineer (FDE)

About Monte Carlo Monte Carlo is the agent trust platform that unifies data and agent observability to monitor, troubleshoot, and improve production AI systems. As enterprises prepare to deploy thousands of agents across business-critical use cases, Monte Carlo provides the reliability infrastructure to support them along this AI transformation, from human-guided agents to fully autonomous operations. Founded in 2019 and backed by leading investors, Monte Carlo empowers data and AI teams to ship trusted AI at scale. Learn more at montecarlodata.com http://montecarlodata.com. ABOUT THE TEAM The FDE team at Monte Carlo is a small, rapid-prototyping unit in the field. The team handles customer-specific technical integration requests without constraining the core product roadmap — FDEs prototype solutions, conduct rapid field testing, and relay what they learn back to Engineering and Product. THE ROLE This is a field engineering role for someone who can sit across from a customer, understand what they're trying to build, and go write it. You'll prototype integrations and connectors based on real customer requests, conduct rapid field testing in live environments, and relay what you learn back to Engineering so the best solutions make it into the core product. You're the link between what customers need today and what Monte Carlo ships tomorrow. WHAT YOU'LL DO - Meet with customers to understand their technical requirements, data environments, and integration gaps — then translate those conversations into working prototypes - Build integrations and connectors tailored to customer data stacks (Snowflake, Databricks, Salesforce, and others) using Python, SQL, and REST APIs - Run rapid field testing in customer environments — ship fast, validate fast, iterate fast - Document what you learn and relay it back to Engineering: what customers are asking for, what worked, what didn't, and why - Work alongside senior FDEs to hand off proven solutions that can scale into the core product - Become a trusted technical contact for your customers — someone they loop in early, not after something breaks WHAT WE'RE LOOKING FOR Field-Facing Engineering Experience 1–3 years in a technical role where you owned deployment or integration work in customer or enterprise environments — implementation engineering, integration engineering, applied engineering, or similar. You've built things that actually ran in production at someone else's company. Python, SQL, and APIs Hands-on with Python and SQL. Comfortable working with REST APIs in real environments, not just in tutorials. You know how to debug integrations when they break in the field. AI Fluency Current on where AI tooling is heading and has applied it in actual work. You're not just familiar with the concepts — you've shipped something with it. Communication That Closes the Loop Strong written and verbal communication. You'll document customer needs, brief Engineering regularly, and be the person customers trust to translate technical complexity into something actionable. THIS IS NOT FOR YOU IF - You want to work on internal tooling with no customer contact - You need a fully defined process before you can ship - Your idea of field work is joining a call and writing a summary - You're looking for a support role — this is a build role WHY MONTE CARLO - You'll see your work in the product: Integrations you prototype in the field directly inform what Engineering builds next — no gap between what you learn and what ships - Real scope, early in your career: Small team, live customer deployments from day one — not a shadow program or a rotation - The space is growing: Every enterprise deploying agents needs reliability infrastructure underneath — Monte Carlo is building that layer - Direct line to Engineering: What you learn in the field goes directly to the people who can act on it - Built to grow: Integrations engineering + field experience + AI fluency is a rare combination; you'll leave with all three