Description: 

The AI Implementation Expert is responsible for guiding the successful deployment of AI solutions from proof-of-concept to full-scale implementation. They work closely with all the stakeholders to ensure AI use cases are properly defined, built, and executed. Their role involves risk management, setting realistic timelines, and ensuring that technical and business objectives are met.

• Mission: 3 months (renewable for the duration of the project).
• Location: Rabat/Casablanca, Morocco.
• Start Date: ASAP.
• Language: Fluent English required (CVs must be submitted in English).
• Work Mode: Hybrid (mix of on-site and remote work).

Key Responsibilities:

• Lead the implementation of AI use cases, ensuring that the solutions meet business requirements.
• Collaborate with business leaders to define AI project scope and success criteria.
• Oversee AI project timelines, resource allocation, and risk management.
• Work closely with the AI Solution Architect to ensure technical feasibility and integration.
• Advise on best practices for AI model development, testing, deployment, and monitoring.
• Ensure alignment between business goals and technical execution of AI solutions.

Skills Needed:

• Extensive experience in the end-to-end implementation of AI solutions (e.g., machine learning models, NLP, predictive analytics). Ability to guide teams through AI solution lifecycle phases.
• Project Management: Ability to manage AI projects from concept to deployment, ensuring they stay within scope, time, and budget.
• Technical Proficiency in AI: Deep understanding of AI/ML models, data pipelines, and deployment techniques. In-depth knowledge of AI tools and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and cloud environments (AWS, Azure, GCP, Oracle) and their AI capabilities.
• Solution Design: Expertise in designing scalable AI solutions that meet business needs.
• Cross-Functional Collaboration: Ability to work with data scientists, developers, business analysts, and other stakeholders to deliver AI projects.
• Change Management: Ability to contribute to the initiatives related to the cultural and organizational changes that come with AI implementation.
• Performance Monitoring: Ability to measure and monitor the performance of AI systems post-deployment, including KPIs and continuous improvement.