EU remote
Senior AI Engineer - HYPR
About this role
Aspire Software is looking for a Senior 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 an AI-first Senior Engineer to help move our team from light AI use to agent-driven, multi-agent development. You'll build AI-powered product features end to end, work agent-first in your own day-to-day development, and share what works so the rest of the team can follow. You're also a strong full-stack engineer with modern stack experience, ready to play a key role in an upcoming ground-up rebuild (v2) of our platform while keeping our existing Java/Oracle platform running in the meantime.
AI-first development: Be a hands-on early adopter: try agentic and multi-agent development workflows on real work, and find what works for our codebase and team. Share what you learn by demonstrating workflows, contributing best practices and documentation, and helping teammates get up to speed. Apply AI across the development lifecycle, from coding and testing to code review, CI/CD, and work on our legacy codebase and platform migration.
AI product engineering: Design, build, and ship AI-powered product features end to end, such as LLM-based assistants and chatbots, summarization, translation, classification, predictive analytics, personalization, anomaly detection, and intelligent automation. Build LLM applications and agents using prompt engineering, retrieval-augmented generation (RAG), tool/function calling, structured outputs, and multi-step agentic workflows.
Evaluate, select, and integrate AI models, APIs, and frameworks into our applications and internal tooling, balancing quality, latency, and cost for each use case. Set up evaluation and quality practices for AI features: test datasets, automated evals, regression testing of prompts and models, and production monitoring for accuracy, latency, and cost. Make AI features safe and reliable with guardrails, data privacy and PII handling, prompt-injection mitigation, and sensible fallback behavior.