AI Solutions Engineer - Agents & Automation - North Macedonia
TypeFull-time job
LocationNorth Macedonia
Posted3 hours ago
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
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
Nice to Have
Experience with the Claude API, Claude Agent SDK, or Claude Code
Working proficiency in TypeScript / JavaScript (Node.js)
Experience with Model Context Protocol (MCP), including building or configuring MCP servers
Experience building tool-using AI agents that run multi-step workflows
Prior solutions engineering, sales engineering, consulting, or agency experience delivering technical projects for multiple clients
Integration experience with common business platforms such as Salesforce, HubSpot, NetSuite, QuickBooks, Microsoft 365, and Google Workspace
Experience with workflow platforms such as n8n, Make, or Zapier, and good judgment on when a workflow should move from no-code to code
Cloud deployment experience (AWS, GCP, or Azure), including containers and serverless functions
Multi-tenant architecture and familiarity with security reviews, SOC 2, or customer data protection requirements
Document and data extraction from invoices, PDFs, and spreadsheets
Background in finance, accounting, or back-office operations
Experience working with distributed, international teams
How We'll Evaluate
Our selection process is designed to understand your experience, how well you understand what you build, and how you communicate your solutions. Expect to:
Have an initial conversation with our recruitment team to discuss your experience, learn more about HQ, and explore whether the role is a good mutual fit.
Complete a practical take-home assessment, followed by a technical interview where you'll demonstrate your solution, walk us through your code and implementation decisions, and answer questions about how it works. We'll also have a short, practical conversation about SQL.
Meet with our CEO in the final round to discuss your experience, expectations, and potential fit with the team.
Benefits
🌍 Work fully remotely in a collaborative environment
🤖 Build and apply your AI engineering expertise by designing AI agents and automation solutions for real-world business challenges
💼 Work directly with leading U.S. cannabis companies, helping build solutions that improve how their teams operate and scale
📈 Grow your career through hands-on ownership, exposure to emerging AI technologies, and continuous learning
Our Values
We are guided by curiosity, collaboration, and persistence. We seek to understand deeply, work collectively to solve complex challenges, and remain resilient in pursuit of meaningful, long-term impact. These principles shape how we operate as a team and how we support the success of our clients.
👉 Take a look at this short video 🎥 featuring a few words from the CEO about our company, industry insights, and founding HQ!
Looking forward to meeting you!
Originally posted on Himalayas
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
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
Nice to Have
Experience with the Claude API, Claude Agent SDK, or Claude Code
Working proficiency in TypeScript / JavaScript (Node.js)
Experience with Model Context Protocol (MCP), including building or configuring MCP servers
Experience building tool-using AI agents that run multi-step workflows
Prior solutions engineering, sales engineering, consulting, or agency experience delivering technical projects for multiple clients
Integration experience with common business platforms such as Salesforce, HubSpot, NetSuite, QuickBooks, Microsoft 365, and Google Workspace
Experience with workflow platforms such as n8n, Make, or Zapier, and good judgment on when a workflow should move from no-code to code
Cloud deployment experience (AWS, GCP, or Azure), including containers and serverless functions
Multi-tenant architecture and familiarity with security reviews, SOC 2, or customer data protection requirements
Document and data extraction from invoices, PDFs, and spreadsheets
Background in finance, accounting, or back-office operations
Experience working with distributed, international teams
How We'll Evaluate
Our selection process is designed to understand your experience, how well you understand what you build, and how you communicate your solutions. Expect to:
Have an initial conversation with our recruitment team to discuss your experience, learn more about HQ, and explore whether the role is a good mutual fit.
Complete a practical take-home assessment, followed by a technical interview where you'll demonstrate your solution, walk us through your code and implementation decisions, and answer questions about how it works. We'll also have a short, practical conversation about SQL.
Meet with our CEO in the final round to discuss your experience, expectations, and potential fit with the team.
Benefits
🌍 Work fully remotely in a collaborative environment
🤖 Build and apply your AI engineering expertise by designing AI agents and automation solutions for real-world business challenges
💼 Work directly with leading U.S. cannabis companies, helping build solutions that improve how their teams operate and scale
📈 Grow your career through hands-on ownership, exposure to emerging AI technologies, and continuous learning
Our Values
We are guided by curiosity, collaboration, and persistence. We seek to understand deeply, work collectively to solve complex challenges, and remain resilient in pursuit of meaningful, long-term impact. These principles shape how we operate as a team and how we support the success of our clients.
👉 Take a look at this short video 🎥 featuring a few words from the CEO about our company, industry insights, and founding HQ!
Looking forward to meeting you!
Originally posted on Himalayas
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