Aspire Software is looking for a AI 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 rework Automate ticket quantification and code impact analysis — surface affected code areas and generate initial requirement breakdowns Automate the dev lifecycle upstream: requirements validation, ticket quality gates, and spec generation Security Engineering Implement automated CI/CD failure analysis — root cause identification, proposed fixes, and validated resolutions Run automated dependency vulnerability scans and implement validated fixes Deploy OWASP Top 10 automated scanning with proposed and validated security fixes Build log analysis (SIEM/CloudTrail) with smart anomaly alerting beyond what standard tooling provides Automate client security audit questionnaire completion from internal knowledge bases — with mandatory R&D review before any submission AI 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 time Automate 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 infrastructure Consolidate product documentation; auto-suggest documentation updates when code changes Production Infrastructure & Escalations Build and maintain production AI pipelines — model serving, evaluation, monitoring, and shared infrastructure Handle complex escalations from Automation Specialists — workflows requiring custom code or deep integration Strong software engineering in Python and/or TypeScript — clean, tested, production-ready code Hands-on experience building and deploying LLM-based applications in production RAG architecture, prompt engineering, and AI evaluation frameworks API design, microservices, and CI/CD pipelines (GitHub Actions, GitLab CI) Security engineering awareness — OWASP, dependency scanning, and SIEM tooling are a strong advantage Collaborative — works with engineering teams, not as an isolated AI function Nice to Have MLOps/LLMOps: model serving, evaluation, monitoring, and versioning Vector databases (Pinecone, Weaviate, Qdrant, pgvector) Infrastructure-as-code, container orchestration, multi-model architectures Experience with enterprise SIEM tooling (Wazuh, CloudTrail, or equivalent)
