AI Solutions Engineer - Agents & Automation

HeadQuarters; México; On-site; MXN 2,235,000 - 3,352,000; Full time

Job summary

HeadQuarters is seeking an AI Solutions Engineer to design and build AI agents and automations for Cannabis/CPG clients. You will work from discovery through delivery, translating frontline workflows into explicit logic and production-ready code, while ensuring reliability and isolation of customer data.

You’ll partner with sales and operations staff to scope, build, and deploy multi-step workflows, using Python, SQL, and browser automation to integrate diverse tools and systems.

Qualifications

• 3+ years of professional software/automation engineering experience.
• Proof of recent production LLM workflows built on Claude/OpenAI.
• Strong Python: structure, test, and debug your code.
• Strong SQL: joins, CTEs, window functions, data quality.
• Browser automation experience with Playwright or similar tool.
• Experience with RESTful APIs.

Responsibilities

• Design and build AI agents and automations for external customers.
• Translate workflows and rules into explicit logic at production scale.
• Create proofs of concept and demos to show agent capabilities.
• Provide build/run cost estimates for pricing considerations.
• Write production-quality Python for integrations and tooling.
• Ensure deployments isolate customer data and credentials.

Skills

Python programming

SQL

Browser automation

REST APIs

Customer communication

Tools

Playwright

OpenAI / Claude (LLMs)

Job description

About Us

HeadQuarters is a global start-up that partners with US cannabis companies to provide support in finance, sales operations, and logistics. We are currently seeking an AI Solutions Engineer to join our growing product development team.

The Role

We're hiring an engineer to design and build AI agents and automations for our external customers in the fast growing Cannabis / CPG industry. You'll partner with our team from the first conversation through delivery: understanding a customer's workflows, scoping what an agent should own, building it on top of their systems, and making sure it runs reliably in production. The most important part of this job is learning how a customer's work actually gets done: sitting with their frontline staff, earning their trust, and turning unwritten rules and exceptions into logic a system can follow. Once an agent is live, you'll hand it off to one of our Operations Managers, who oversee the agent's day-to-day work and output on the customer's behalf. Every customer runs on a different stack, so this role calls for real engineering depth, and you don't need a long AI resume to have it. We're looking for a strong developer who has recently put LLM-powered workflows into production. Plenty of people can prompt their way to a working prototype. We need someone who can own the code behind it, troubleshoot it inside an unfamiliar environment, and harden it so it holds up long after you've moved on to the next customer.

What You'll Do

• Discovery and Scoping (with the AI Sales Lead)
• Sit with customers' frontline staff to learn how the work actually gets done, capture the rules and exceptions that live in people's heads, and translate them into explicit logic
• Build trust with the people whose work you're automating, and treat them as the experts on their process
• Assess technical feasibility and identify which tasks are good candidates for an agent and where human review should stay in the loop
• Translate customer needs into solution designs, effort estimates, and clear scope
• Build proofs of concept and demos that show customers what an agent can do with their own workflows
• Provide build and run cost estimates (model usage, infrastructure, maintenance) to support pricing
• Join customer discovery calls to understand their workflows, systems, data, and pain points
• Build and Deploy Design, build, and deploy AI agents and automations that handle multi-step workflows across customer operations
• Build and configure integrations that link each customer's tools, such as CRMs, ERPs, accounting platforms, email, messaging, and document storage, using APIs where they exist and browser automation where they don't
• Write production-quality Python for integrations, data transformation, and agent tooling
• Use SQL to validate agent outputs, reconcile records across systems, and measure accuracy
• Build error handling, retries, idempotency, logging, alerting, and graceful fallbacks into everything you ship
• Create testing and evaluation processes for each agent: test sets, regression checks, and accuracy tracking before go-live and after every change
• Architect deployments so each customer's data, credentials, and permissions stay fully isolated

Handoff and Support

• Hand off live agents to Operations Managers with runbooks, monitoring dashboards, review queues, and clear escalation paths
• Train Operations Managers to supervise agent output, spot problems early, and handle routine exceptions without engineering help
• Serve as the technical escalation point when an agent fails or a customer's systems change
• Use what you learn from each deployment to build reusable components, templates, and deployment patterns that make the next customer faster to deliver
• Measure and report impact for each customer against a baseline: hours saved, turnaround time, error rates, and cost per run

First-Year Mission

• Your first year is measured by the operating results customers see, not by how many agents you deliver.
• Establish a baseline for every customer workflow you take on: hours spent, turnaround time, error rates, and cost
• Deliver measurable, sustained improvements against those baselines for customers across multiple industries
• Shorten the time from signed agreement to measurable customer results
• Hand off agents that Operations Managers run day to day with minimal engineering involvement
• Earn renewals and expansions because the results hold up over time 3+ years of professional software engineering, solutions engineering, or automation engineering experience

Requirements

• 3+ years of professional software engineering, solutions engineering, or automation engineering experience
• Proof of recent production LLM workflows : something you built with Claude, OpenAI, or a similar platform that real users rely on
• Strong Python : you structure, test, and debug your code, and you understand what AI-generated code is doing before you ship it
• Strong SQL : joins, CTEs, window functions, and tracking down data quality problems
• Browser automation experience with Playwright or a similar tool, for systems with limited or no APIs
• Solid experience with REST