AI Solutions Engineer – Agents & Automation

Role Overview

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.

What You Will Do

Design, build, and deploy AI agents and automations that handle multi-step workflows across customer operations. You’ll also build and configure integrations, write production-quality Python, and use SQL to validate agent outputs.

Why It Might Be a Fit

We’re looking for a strong developer who has recently put LLM-powered workflows into production. You’ll have the opportunity to grow your career through hands-on ownership, exposure to emerging AI technologies, and continuous learning.

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 APIs, webhooks, OAuth, pagination, and rate limits across a wide range of third-party platforms
• A track record of troubleshooting production failures: reading logs and stack traces, isolating root causes, and fixing them permanently
• Comfort with Bash and the command line for deployment, scheduling, and debugging
• Git and version control as a standard part of how you work
• Workflow discovery skills: you can interview the people who do the work, uncover the steps and exceptions they don’t think to mention, and turn tacit knowledge into explicit rules
• Operator empathy: you build trust with frontline staff, treat them as the experts on their work, and explain technical tradeoffs in plain language
• Comfort in customer-facing settings: you can run a technical conversation with a customer and push back on scope when needed
• Strong documentation habits, since the people running your agents day to day won’t be engineers

Benefits

• Opportunity to work with emerging AI technologies
• Continuous learning and growth opportunities

Tagged as: Python, SQL