Requirements
Proven track record of building products, tools, workflows, automations, startups, or side projects that went live and delivered measurable valueStrong working knowledge of AI and machine learning tools, large language models and agent frameworksExperience building AI agents, workflow automations or AI powered internal toolsExperience designing or improving workflows across software or product delivery lifecycles, including the use of AI tools, agents, or automations to improve speed, quality, and operational leverageAbility to go from zero to functional prototype quicklyStrong systems thinking and comfort working across tools, processes and teamsComfort with ambiguity and the ability to help define the problem as well as the solutionBias toward action, with discipline around quality, evaluation and operational safetyAbility to work directly with business stakeholders and translate operational pain points into practical AI solutionsNice to haveExperience building AI agents or service automation workflowsExperience in hospitality, retail, vertical SaaS, or B2B software environmentsExperience working across multiple product lines, business units or operational functionsFamiliarity with support operations, CRM workflows, or Professional Services delivery modelsExperience with analytics, reporting and operational performance measurementA bachelor’s degree in Artificial Intelligence, Software Engineering, Computer Science, Robotics, Mechanical Engineering or a related discipline
Description
Aspire Software is looking for a AI Automation Engineer 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 the job: The AI Automation Engineer is responsible for designing, deploying and improving AI agents and agentic workflows that automate routine operational work, augment teams, and improve speed, quality and scalability across the business.This role is not about building disconnected experiments. It is about delivering real workflow change in priority areas, initially focused on Support, Sales, Professional Services and internal operational workflows. The role works closely with functional leaders and frontline teams to identify high value use cases, redesign workflows, deploy safe and useful agents, and ensure adoption in day-to-day operations.The AI Automation Engineer helps shift repetitive, rules based and data intensive work to AI agents, while preserving human ownership of judgment, sensitive customer interactions, escalations, quality assurance, exception handling and continuous knowledge improvement.What you will doDesign and deploy AI agentsDesign and deploy AI agents that support operational work, including support triage, internal workflow automation, sales support, onboarding tasks, knowledge handling, reporting and analysisBuild agents and workflows that integrate with CRM, ERP, internal tools, SaaS platforms and APIsUse multi step workflows, tool using agents, decision logic and orchestration patterns where appropriateMove quickly from idea to pilot to production while maintaining clear standards for quality and reliabilityDesign and implement AI agents and workflow automations that support PDLC activities such as requirements drafting, backlog preparation, technical research, code generation, test generation, documentation, defect triage, release support, and post release analysisIdentify manual or repetitive PDLC tasks and convert them into scalable AI assisted workflows, including integrations with tools such as Jira, Confluence, GitHub, service platforms, and internal systemsPartner with Product, Engineering, QA, Support, and other stakeholders to pilot, refine, and scale agentic PDLC workflows across teamsRedesign business workflowsIdentify high value automation opportunities with functional leaders and operatorsRedesign workflows so repetitive and rules-based work is handled by AI wherever safe and practicalSupport the shift to AI first intake, triage, knowledge retrieval, simple response handling and case preparation in service operationsHelp teams redesign work around a clear human and agent division of labourHelp establish safe and scalable standards for AI use across the PDLC, including review points, escalation paths, auditability, fallback handling, and quality controls for AI generated artefactsGovernance, quality and evaluationWork within approved tooling standards, data and security guardrails, and design standards for agents and evaluationEstablish monitoring, fallback handling, escalation logic, prompt and workflow controls, and human review mechanismsTest workflows with real users and real operational scenarios before scalingMeasure agent quality, workflow success, containment, handoff quality and operational valueContinuously improve workflows based on usage data, user feedback and service outcomesAdoption and enablementPartner with functional leaders and frontline teams to support rollout and adoptionDocument new ways of working and help train users on how to work effectively with AI agentsSurface workflow risks, resistance points and improvement opportunitiesContribute reusable prompts, components, patterns and playbooks to the central AI capabilityCross functional deliveryCollaborate with Product, Engineering, Support, Sales and Professional Services teamsHelp prioritise use cases based on measurable business value, feasibility and operational readinessSupport the central AI capability with value tracking, reporting and continuous learning across initiativesWhat success looks likeReduction in manual work and process cycle timeImproved support response times and service leverageWorkflow success rate and agent task completion qualityTicket containment or deflection where relevantHandoff quality for escalated or human reviewed workUser adoption of AI enabled workflowsHours saved and team capacity createdContribution to service, productivity and customer outcomesReduced manual effort across PDLC activities, faster movement from idea to release, improved quality of delivery artefacts, and stronger adoption of AI enabled workflows across Product and Engineering teams