AI Solutions Engineer – Agents & Automation
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Job Description
AI Solutions Engineer building production AI agents and automations for a startup supporting U.S. cannabis companies. Integrating customer systems and delivering measurable operational improvements.
Responsibilities:
- Join customer discovery calls to understand workflows, systems, data, and pain points
- Learn frontline processes, rules, and exceptions and translate them into explicit logic
- Assess technical feasibility and determine where human review should remain in the loop
- Translate customer needs into solution designs, estimates, and scope
- Build proofs of concept and customer demos
- Estimate model usage, infrastructure, and maintenance costs
- Design, build, and deploy AI agents and automations for multi-step customer operations workflows
- Integrate CRMs, ERPs, accounting platforms, email, messaging, and document storage using APIs or browser automation
- Write production-quality Python for integrations, data transformation, and agent tooling
- Use SQL to validate outputs, reconcile records, and measure accuracy
- Implement error handling, retries, idempotency, logging, alerting, and graceful fallbacks
- Create testing and evaluation processes, regression checks, and accuracy tracking
- Architect isolated customer data, credentials, and permissions
- Hand off live agents to Operations Managers with runbooks, dashboards, review queues, and escalation paths
- Train Operations Managers to supervise agent output and handle routine exceptions
- Serve as technical escalation point for agent failures and customer system changes
- Build reusable components, templates, and deployment patterns
- Measure and report customer impact, including hours saved, turnaround time, error rates, and cost per run
- Establish workflow baselines and deliver sustained measurable improvements
- Shorten time from signed agreement to measurable customer results
- Support renewals and expansions through durable customer outcomes
Requirements:
- Several years of professional software engineering, solutions engineering, or automation engineering experience
- Proof of recent production LLM workflows built with Claude, OpenAI, or a similar platform that real users rely on
- Strong Python skills, including structuring, testing, and debugging code
- Strong SQL skills, including joins, CTEs, window functions, and data quality troubleshooting
- Browser automation experience with Playwright or a similar tool
- Experience with REST APIs, webhooks, OAuth, pagination, and rate limits
- Track record of troubleshooting production failures using logs and stack traces
- Comfort with Bash and the command line
- Git and version control experience
- Workflow discovery skills and ability to turn tacit knowledge into explicit rules
- Operator empathy and ability to explain technical tradeoffs in plain language
- Comfort in customer-facing technical settings
- Strong documentation habits
- Nice to have: Claude API, Claude Agent SDK, or Claude Code experience
- Nice to have: TypeScript/JavaScript and Node.js proficiency
- Nice to have: Model Context Protocol (MCP) experience
- Nice to have: tool-using AI agent experience
- Nice to have: solutions engineering, sales engineering, consulting, or agency experience
- Nice to have: Salesforce, HubSpot, NetSuite, QuickBooks, Microsoft 365, or Google Workspace integration experience
- Nice to have: n8n, Make, or Zapier experience
- Nice to have: AWS, GCP, or Azure deployment experience, including containers and serverless functions
- Nice to have: multi-tenant architecture and familiarity with security reviews, SOC 2, or customer data protection
- Nice to have: document and data extraction experience
- Nice to have: finance, accounting, or back-office operations background
- Nice to have: distributed, international team experience
- Must walk through a shipped LLM workflow or automation
- Must complete a practical debugging and hardening exercise
- Must participate in a mock customer discovery conversation
Benefits:
- Work fully remotely in a flexible and collaborative environment
- Build and apply AI engineering expertise by designing AI agents and automation solutions for real-world business challenges
- Work directly with leading U.S. cannabis companies
- Career growth through hands-on ownership, exposure to emerging AI technologies, and continuous learning
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