About this role
Aspire Software is looking for a AI Product 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:
We’re looking for AI Engineers to build the technically complex systems that no-code tools can’t handle across our portfolio SaaS companies. You’ll tackle the hardest automation problems: CI/CD and security engineering, upstream developer tooling, AI product features, and log intelligence. You’ll write production code, build infrastructure, and handle escalations from Automation
Specialists when workflows require custom code or deep system integration.
WHAT YOU WILL DO
Developer Tooling & Ticket Quality
Build AI pre-screening for incoming R&D tickets — flag vague requirements, missing acceptance criteria, and ambiguous descriptions before developer assignment; root cause of downstream reworkAutomate ticket quantification and code impact analysis — surface affected code areas and generate initial requirement breakdownsAutomate the dev lifecycle upstream: requirements validation, ticket quality gates, and spec generationSecurity Engineering
Implement automated CI/CD failure analysis — root cause identification, proposed fixes, and validated resolutionsRun automated dependency vulnerability scans and implement validated fixesDeploy OWASP Top 10 automated scanning with proposed and validated security fixesBuild log analysis (SIEM/CloudTrail) with smart anomaly alerting beyond what standard tooling providesAutomate client security audit questionnaire completion from internal knowledge bases — with mandatory R&D review before any submissionAI Product Features
• Build and maintain AI-powered product features: LLM integrations, RAG systems, intelligent
automation, and custom AI agents
• Evaluate and integrate LLM providers based on cost, quality, and latency
• Establish lightweight tooling governance — new tools can be challenged and validated, not
unilaterally adopted
Log Intelligence & Deployment Infrastructure
Build AI-assisted log file collection, parsing, and pre-analysis to accelerate ticket resolution and reduce manual investigation timeAutomate CI/CD deployment pipelines and build error scraping and real-time KPI reporting pipelines
Knowledge Systems & RAG
Build internal ticket knowledge systems from historical data — pre-classification, pre-routing, and automated resolution infrastructureConsolidate product documentation; auto-suggest documentation updates when code changes
Production Infrastructure & Escalations
Build and maintain production AI pipelines — model serving, evaluation, monitoring, and shared infrastructureHandle complex escalations from Automation Specialists — workflows requiring custom code or deep integration
Requirements
Strong software engineering in Python and/or TypeScript — clean, tested, production-ready codeHands-on experience building and deploying LLM-based applications in productionRAG architecture, prompt engineering, and AI evaluation frameworksAPI design, microservices, and CI/CD pipelines (GitHub Actions, GitLab CI)Security engineering awareness — OWASP, dependency scanning, and SIEM tooling are a strong advantageCollaborative — works with engineering teams, not as an isolated AI function
Nice to Have
MLOps/LLMOps: model serving, evaluation, monitoring, and versioningVector databases (Pinecone, Weaviate, Qdrant, pgvector)Infrastructure-as-code, container orchestration, multi-model architecturesExperience with enterprise SIEM tooling (Wazuh, CloudTrail, or equivalent