We are seeking a strategic, adaptable, and highly technical AI Lead to drive the design, architecture, and deployment of enterprise-grade AI solutions. The role bridges business objectives and deep technical execution, taking ownership of end-to-end AI initiatives while leading cross-functional teams and building scalable, production-ready systems in a dynamic environment.

The Key Responsibilities are:

Strategic Roadmap Execution: Partner closely with Product leadership to translate complex business KPIs into concrete, actionable technical roadmaps.
Stakeholder Coordination: Act as the primary technical point of contact, organizing cross-functional alignments and managing seamless communication across diverse business stakeholders.
Team Mentorship: Direct and mentor cross-functional engineering and AI talent, establishing best practices across the machine learning lifecycle and fostering a culture of rapid experimentation.
Process Optimization: Establish organizational frameworks to navigate early-stage or unstandardized technical environments, turning ambiguity into structured, scalable engineering workflows.
Agentic AI Ecosystems: Direct the architecture and end-to-end development of custom Agentic AI ecosystems tailored for operational and executive workflows.
Advanced Orchestration: Implement advanced reasoning and decision-support systems, utilizing various orchestration frameworks to progress solutions from simple data retrieval to autonomous task execution.
Predictive & Generative Modeling: Oversee the execution of high-performing models, including predictive forecasting architectures, Natural Language Processing (NLP), and Large Language Model (LLM) RAG infrastructures.
AI Democratization: Advocate for and deploy self-service AI and analytics tools (such as AWS Q or QuickSight) to empower non-technical teams, successfully mitigating data engineering bottlenecks.
End-to-End Lifecycle Management: Design, establish, and maintain standardized DataOps and MLOps production pipelines to streamline the machine learning lifecycle.
CI/CD/CT Automation: Formulate clear standards for continuous integration, continuous deployment, and automated retraining pipelines to accelerate experiment-to-deployment cycles while ensuring absolute reproducibility.
Cloud Architecture: Architect scalable, secure, and production-ready applications, primarily leveraging cloud ecosystems.

Requirements

A Bachelor’s, Master’s, or PhD in Computer Science, Artificial Intelligence, Data Science, or a related quantitative engineering field is required.
Minimum 8+ years of professional experience in machine learning, AI engineering, or related domains, including leadership experience.
Startup & Immature Environment Exposure: Proven experience navigating an unstandardized or early-stage technical ecosystem, with a demonstrated ability to establish structure and drive operational maturity.
Leadership Experience: A robust background in technical leadership, team management, or directing cross-functional engineering pods.
Strong Coordination Skills: Exceptional organizational capabilities with a track record of coordinating and aligning priorities between business owners and technical teams.
ML & MLOps Lifecycle Architecture: Deep expertise in core Machine Learning modeling and the end-to-end MLOps lifecycle, including CI/CD/CT pipelines and production deployment.
Predictive Modeling & Statistical Inference: Advanced experience deploying classical machine learning architectures, including tree-based ensembles (e.g., XGBoost, Random Forest) for large-scale classification, regression, and predictive tasks.
Agentic Systems & Vector Search: Hands-on experience designing agentic workflows using orchestration frameworks such as Amazon AgentCore and managing vector datasets with Amazon OpenSearch.
Enterprise Analytics & Data Democratization: Strong experience leveraging BI tools such as Amazon QuickSight to enable self-service analytics across business functions.
Production Reliability & Observability: Experience implementing production-grade monitoring, observability, and debugging tools (e.g., DataDog) for system uptime and data drift detection.
Cloud Infrastructure (AWS-Centric): Strong architectural knowledge of AWS ecosystems, with additional exposure to multi-cloud environments such as Microsoft Azure considered a plus.
Production Engineering Foundations: Solid experience building production-grade systems, including scalable data ingestion, containerization (Docker), and database engineering across SQL and NoSQL systems.
Work Schedule: Standard working hours aligned with business needs in Lebanon.
Excellent English communication skills.

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