# AI Developer - InQ

**Company:** [Valsoft Corporation](http://jobs.workable.com/companies/i7bvgtD9zMBw8pksaJ6inV.md)
**Location:** Beirut, Lebanon
**Workplace:** hybrid
**Employment type:** Full-time
**Department:** Research & Development

[Apply for this job](http://jobs.workable.com/view/0f540009-b169-4574-94ab-672194c19f2f)

## Description

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.