Forward Deployed Engineer - MX

About the role

FORWARD DEPLOYED ENGINEER - MEXICO

About the Company

We're partnering with an AI-native operating system for supply chains used by wholesalers, manufacturers, retailers, and traders. The platform integrates logistics and procurement data through connections with internal and external systems, enabling predictive analytics and automated workflows that improve fill rates, reduce inventory levels, and optimize cash-to-cash cycle time.

By leveraging predictive analytics and automation, the company helps large enterprises reduce costs, unlock additional revenue, and prevent under- or over-stocking across complex global supply chains.

Role Overview

The Forward Deployed Engineer (FDE) is the person who makes the platform work in the real world. This role sits at the intersection of engineering, client operations, and AI implementation — and owns the technical execution of how the platform gets built into each client's environment.

The FDE works directly with clients and the Implementation team, learns how each client's systems and operations actually run, and builds the integrations, automations, and AI solutions that make the platform mission-critical. This is a first-of-its-kind role at the company — the person who takes it will be defining what forward deployment looks like here.

Key Responsibilities

- Build integrations between the platform and client systems — ERPs, WMS, TMS, data warehouses — writing real code to connect, extract, and transform data across environments

- Deploy alongside clients in their environment — understand how their teams actually operate, where data breaks, and what needs to be built or configured to run reliably

- Design, implement, and iterate on AI workflows using the company's platform — agentic flows, prompt engineering, context design, and output evaluation — tuned to each client's specific supply chain use cases

- Own the reliability, performance, and continuous improvement of live deployments — monitor outputs, identify failure modes, and fix them without waiting to be asked

- Translate what you build and learn in the field into structured product feedback, technical documentation, and reusable playbooks — so the next deployment starts smarter than the last

Profile We're Looking For

- You build things: Min. 2-4 years of hands-on engineering experience — Python, REST APIs, SQL — and a portfolio of real integrations or systems you've shipped, not just designed

- You operate in ambiguity: comfortable walking into a client environment without a complete spec, asking the right questions, and figuring out what needs to be built — without waiting for perfect requirements

- You understand data end-to-end: how it flows across systems, where it breaks, and what it takes to make it clean and reliable enough for AI to use — SQL or equivalent is second nature

- You have a working understanding of AI and LLMs: enough to design prompts, evaluate outputs critically, and know when a model result looks right but isn't