ML/AI Engineer - QMTC

Aspire Software is looking for a Senior Software 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 Role We’re looking for a mid-to-senior ML/AI Engineer to join the team building Qmatic’s next generation of AI-driven products. This is a hands-on machine learning and AI engineering role, not a general fullstack position. You’ll design, train, evaluate, and ship the models and AI capabilities behind our products: forecasting and recommendation models on our data platform, and LLM-based features such as retrieval and agentic, self-service flows. You’ll work close to the product and the data, and close to the people who use and sell it: developers, product managers, sales teams, partners, and sometimes the end customer directly. This role sits at the intersection of engineering and business. You’ll join demos, translate technical trade-offs and model results into plain language for non-technical stakeholders, and bring feedback from the field back into how we build. In practice that spans the models behind forecasting and recommendations, the data and retrieval work behind our chat product’s knowledge base, and the agentic, self-service capabilities we’re building next, letting customers complete a task end-to-end rather than just getting an answer. Responsibilities Design, train, evaluate, and ship machine learning models, such as forecasting, classification, and recommendation, that power predictive and recommendation features across our products Choose approaches on evidence: compare candidate models against baselines, run experiments, and document why a choice was made, with clear evaluation criteria and monitoring once a model is in production Integrate AI/LLM capabilities into product workflows — chat, automation, personalization, and agentic flows that complete tasks end-to-end (booking, rescheduling, account changes) — as we move further into self-service Build, maintain, and debug the data and feature pipelines that feed our models and AI products — ingestion, transformation, and data quality checks across a medallion (bronze/silver/gold/platinum) architecture Join client and stakeholder conversations — demos, product launches, requirements discussions, troubleshooting calls — and translate customer and partner feedback into concrete product and engineering decisions, working closely with Product Management Write clean, maintainable, well-tested code, and help define architecture, patterns, and technical direction for new products as they take shape, rather than inheriting one fixed stack Support and troubleshoot production issues, including model and data problems and client-facing incidents when needed Requirements 3+ years of professional experience building and shipping machine learning or AI systems in production, with strong engineering fundamentals independent of AI-assisted tooling Strong applied ML experience across the full lifecycle: you have trained, fine-tuned, and evaluated models (e.g. classification, regression, or forecasting), and can reason about data quality, features, and model performance. You compare alternatives against baselines and can explain why you chose a model, rather than defaulting to what is popular Strong Python and the open-source ML ecosystem (e.g. scikit-learn, XGBoost/LightGBM, statsmodels), solid SQL, and API design, so the models you build can be served and integrated into real products Real, hands-on experience building with AI/LLM capabilities in production — able to point to AI-driven features you’ve actually shipped (e.g. RAG pipelines, vector databases, or other LLM-adjacent infrastructure), not just curiosity about AI. Comfortable using AI coding tools day-to-day (e.g. Claude, Cursor, GitHub Copilot) Experience with cloud platforms (Azure, AWS, or Google Cloud), including building cloud data pipelines and ETL across a medallion (bronze/silver/gold/platinum) architecture, and deploying and monitoring models in production Excellent communication skills — able to explain technical concepts clearly to clients, partners, and non-technical stakeholders, and comfortable in customer-facing settings (demos, calls, workshops) A flexible, product-minded, and collaborative approach — curious and accountable, comfortable moving between products and technologies as our portfolio grows rather than staying fixed to one stack, and taking initiative across teams Fluent in written and spoken English Reliable and deadline-driven — sets realistic estimates, hits them, and raises risk early when a date won’t be met An active GitHub profile or portfolio of personal/side projects — e…