About this role
Aspire Software is looking for an AI Automation & Agent Builder to join our team in Lebanon.
Here is a little window into our company: Aspire Software operates and manages wholly owned software companies, providing mission-critical solutions across multiple verticals. By implementing industry best practices, Aspire delivers a time sensitive integration process, and the operation of a decentralized model has allowed it to become a hub for creating rapid growth by reinvesting in its portfolio.
About theAI-Driven Automation
The AI Automation & Agent Builder Engineer is an AI-first builder responsible for designing, building, and shipping intelligent agents and automated workflows that replace manual work and generate measurable business value. This role is not a research or experimentation role. AI Automation Engineers are embedded directly inside portfolio company teams, understanding real operations, and deploying agents that handle real work — customer service, sales outreach, onboarding, document processing, and more.
Some of what you build will replace manual workflows within Aspire’s portfolio companies. Some of it will become new revenue-generating products sold to their customer bases. Either way, it ships to real users and is measured by real impact. This role is foundational in AI-native organizations and is often the highest-ROI AI position in the company.
Core Responsibilities
Build Internal AI Agent TeamsDesign and deploy AI agents that handle operational work across portfolio companies — including customer support (Tier 1 & 2), SDR and sales outreach, onboarding automation, content workflows, and engineering maintenanceAutomate core business processes: CRM updates, document processing, reporting, and analytics pipelinesWork directly with portfolio company operators to identify the highest-value automation opportunitiesMeasure impact in hours of labor replaced, team output increased, or cost savings generated
Build External AI Products
Develop new AI-powered products such as vertical voice agents, AI copilots, workflow automation tools, and AI documentation systems tailored to vertical marketsOwn the product from prototype through launch, including reaching first paying customersIntegrate AI capabilities with existing systems: CRM, ERP, internal tools, SaaS platforms, and third-party APIsBuild agentic workflows including multi-agent coordination, decision engines, and tool-using AI agents
Ship and Iterate
Move fast from idea to MVP to production — bias toward working software over documentationRun beta tests with real users, gather feedback, and improve continuouslyOptimize AI agent performance: latency, cost, accuracy, and human intervention rateEnsure production reliability through monitoring, evaluation, and model lifecycle managementCollaborate cross-functionally with product, engineering, and go-to-market teams
What This Role Is Not
Not a research or proof-of-concept role — everything you build ships to real usersNot a traditional backend or systems engineering role focused on ticket executionNot bound by story points, sprint rituals, or rigid SDLC stagesNot limited to internal tooling — what you build may become a product customers pay forRequirements
Core Capabilities
Proven track record of building products, tools, automations, or side projects that went live and created real impactStrong working knowledge of AI and machine learning tools, large language models (LLMs), and agent frameworksAbility to go from zero to functional prototype quicklyComfort with ambiguity — you will often be defining the problem as much as the solutionBias toward action over planningTechnical Skills
Hands-on experience with LLM integrations, RAG architecture, and prompt engineeringProficiency with workflow orchestration and automation platforms (e.g. n8n, Zapier, Make, LangChain, CrewAI)Ability to build and maintain API integrations across CRM, ERP, and SaaS systemsUnderstanding of vector databases, embedding pipelines, and model hostingStrong debugging and problem-solving skills across full AI system stacksMindset & Approach
Outcome-oriented: you measure success in labor hours saved, revenue generated, and adoption rate — not tickets closedStrong operational intuition — ability to understand a business workflow and identify where AI creates the most leverageComfortable working directly with business operators, not just engineering teamsAbility to reason about AI system limitations, failure modes, and appropriate human-in-the-loop designKey Performance Indicators (KPIs)
Automation Impact
Number of business processes automatedHours of manual labor saved per monthReduction in manual task volume across portfolio companiesAgent Performance
Agent task success rateAgent accuracy and hallucination rateHuman intervention rate (lower = better)Efficiency & Business Value
Cost savings generated through automationResponse time improvements across automated workflowsOperational throughput increaseDelivery & Adoption
Number of AI agents and automations shipped per quarterTime from idea to production deploymentNumber of teams actively using AI agents built by this roleInternal workflow adoption rate