AI Engineer

The environment includes an enterprise LLM platform (based on Langdock) with 100+ existing assistants, alongside a full Microsoft ecosystem including Copilot, Teams, SharePoint, Power Platform, and Azure. This is a hands-on role focused on building functional AI solutions, enabling self-service capabilities, and supporting scale across markets. Responsibilities Workstream 1 - Assessment & Onboarding (Weeks 1-6)

• Support technical assessment of 100+ existing AI assistants and document configurations and usage patterns.
• Set up development and testing environments for upcoming use cases.
• Begin measurement dashboard design and identify relevant data sources.

Workstream 2 - Country Deep Dive Implementation (Weeks 5-13)

• Build and configure AI assistants using enterprise LLM tooling, aligned with use case designs.
• Develop prompt templates, test scenarios, and QA processes.
• Produce 10-15 training videos, tutorials, and best-practice content for non-technical users.
• Create and populate a centralized use case repository with 20-30 documented cases.
• Support on-the-job training sessions with demos and hands-on guidance.

Workstream 3 - Governance & Readiness to Scale (Weeks 1-17)

• Build adoption dashboards and measurement systems (Power BI or similar).
• Build a 'Usage Assistant' Teams Bot to help users discover and adopt assistants.
• Monitor usage patterns, identify underperforming assistants, and propose improvements.
• Support cleanup and consolidation activities based on data insights.

• Solid AI/ML engineering background with hands-on experience building LLM-based assistants (prompt engineering, RAG, agent architectures).
• Proficiency in the Microsoft ecosystem: Azure, Power Platform, SharePoint, Teams.
• Dashboard development experience (Power BI or similar).
• Ability to create clear, engaging training content, especially video tutorials.
• Experience building or integrating APIs, bots, or conversational interfaces.
• Fluent English.

Desired

• Experience with Langdock or similar AI assistant platforms.
• Knowledge management & documentation best practices.
• Experience building self-service solutions for non-technical teams.
• Experience in retail or FMCG environments.