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Location
In office (Beirut)
Employment
Full-Time
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Valsoft Corporation is a Canadian software holding company specializing in the acquisition, development, and management of vertical market software (VMS) businesses. Headquartered in Montreal, Quebec, the company operates under a decentralized, "hold-forever" philosophy, partnering with niche software firms to drive long-term growth and stability.

Aspire Software is looking for a AI Developer 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:

  • Design, build, and ship full stack applications (modern front-end frameworks like React, plus API and service layers) where AI capabilities are core to the product, not bolted on
  • Build LLM-powered features end-to-end: prompt and context engineering, RAG pipelines, structured outputs, and agent workflows integrated with product data and APIs
  • Use AI-assisted development tools (Claude Code, Copilot, Cursor) as a force multiplier - owning architecture and code quality while delegating implementation speed to AI
  • Design and maintain evaluation frameworks for AI features: golden datasets, automated evals, regression testing on prompt/model changes, and production monitoring for drift and quality
  • Own features from ambiguous requirements through production deployment - including CI/CD, observability, and post-launch iteration

Requirements

AI Product Development

  • Hands-on experience integrating LLM APIs (OpenAI, Anthropic, or similar) into production applications used by real customers.
  • Prompt engineering fluency: system prompts, few-shot examples, structured outputs, tool/function calling, chain-of-thought patterns.
  • Understanding of token economics: cost per call, context management, model selection tradeoffs across feature types.
  • Experience building quality evaluation and output validation frameworks around AI features.
  • Awareness of AI-specific risks: hallucination, prompt injection, data leakage, output inconsistency.
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