The Machine Learning / AI Analyst acts as the key interface between business stakeholders and technical teams within the RIO MVP initiative. The role ensures that business needs are clearly understood, translated into functional specifications, and reflected in the metadata models, validation workflows, and RAG/LLM interactions.
The analyst contributes directly to the quality, accuracy, and relevance of the AI copilots and the overall RAG engine.
Primary Tasks and Responsibilities
• Collect, analyze, and formalize user needs from MLOZ stakeholders, business teams, and internal contributors.
• Define usage scenarios, extraction rules, validation workflows, and expected behaviors for the AI copilot.
• Design prompts, templates, and interaction strategies for LLM/RAG components.
• Write functional specifications for engineering and data teams.
• Validate AI output quality in terms of accuracy, relevance, traceability, sourcing, and consistency.
• Facilitate clear, empathetic, and pedagogical communication between technical teams and business users.
Secondary Tasks and Responsibilities
• Support metadata modeling and documentation of functional logic.
• Contribute to iterative improvements of prompts, RAG configurations, and validation rules.
• Participate in workshops with stakeholders to refine requirements and evaluate prototypes.
• Assist in user testing, feedback collection, and continuous improvement loops.
• Provide functional insights to guide AI/ML engineers during implementation.
Practical experience implementing GenAI projects (LLMs, RAG, copilots, hybrid pipelines).
Strong knowledge of application architecture, data pipelines, and RAG concepts.
Solid technical foundations in Python and SQL.
Proven ability to design effective prompts and LLM interaction strategies.
Understanding of metadata modeling and validation logic design.
Experience working in Agile environments (Scrum, Kanban).
Excellent communication skills with the ability to explain AI concepts clearly to non-technical audiences.
Strong analytical, user-centric, and detail-oriented mindset.
Fluency in French or Dutch with good proficiency in the other national language.
At least 7 years of experience in data architecture and GenAI-related implementations.
At least 7 years of experience using Python and SQL in data or AI contexts.
Ability to translate complex business needs into structured, actionable functional documentation.
Collaborative and constructive approach when working with multidisciplinary teams.
English proficiency as a strong working language.
Experience supporting metadata documentation and functional logic formalization.
Familiarity with structured delivery methodologies beyond Agile.
Certifications in AI, data analysis, or business analysis.
ITIL Foundation certification (around 3 years of experience).
Architecture certifications (TOGAF, CITA, EACOE, Certified SOA Architect, or similar).
