**Job Description
Project/Department Description and Responsibilities**
The AI / ML Engineer is responsible for implementing all technical components required for the RIO MVP delivery. This includes document ingestion, extraction, processing pipelines, embeddings generation, RAG pipeline construction, APIs, evaluation scripts, and integration with the user interface.
Working closely with the AI Architect, the engineer ensures that all components are robust, performant, secure, and aligned with the global solution design.
Primary Tasks and Responsibilities
Develop end to end data pipelines including SharePoint extraction, document parsing, metadata generation, and structured storage.
Build embeddings, RAG indexes, search logic, and response mechanisms for the AI system.
Implement integrations with Azure OpenAI services, internal databases, and API layers.
Collaborate with the Architect to ensure alignment with the global technical vision and solution architecture.
Maintain high code quality through testing, review, documentation, and performance optimization.
Perform a dedicated 5 day ramp up at the start of the mission to configure and validate the technical environment.
Support the team during integrated testing phases and functional validation activities.
Secondary Tasks and Responsibilities
Contribute to continuous improvement of pipelines, performance tuning, and monitoring practices.
Collaborate with ML/AI Analysts to refine extraction logic, metadata models, and RAG optimization strategies.
Assist in debugging, troubleshooting, and root cause analysis across the MVP stack.
Prepare technical documentation and reusable components for future project phases.
Participate in knowledge sharing and internal capability development.
Technical Profile Requirements
Strong proficiency in C#.NET for AI/ML-related data processing and backend development.
Experience in backend engineering using C#, PHP, JavaScript, and relational/non relational databases.
Knowledge of AI algorithms, embeddings, model serving, and deploying AI components into production environments.
Hands-on experience with cloud-based AI services (Azure OpenAI is a strong asset).
Solid understanding of data extraction, processing architectures, and RAG pipeline mechanics.
Non Technical Profile Requirements
Pragmatic mindset with strong problem-solving abilities.
Autonomous, proactive, and capable of delivering within tight MVP timelines.
Strong collaboration skills and ability to work closely with architects, analysts, and cross-functional teams.
High sense of responsibility and discipline in execution and documentation.
Ability to adapt quickly to evolving requirements.
Methodology/Certification Requirements
Experience working in Agile environments and iterative MVP deliveries.
Knowledge of DevOps practices, CI/CD, or cloud engineering methodologies is a plus.
Technical certifications (Azure, .NET, AI/ML) are considered advantageous.
Language Proficiencies
FR and NL: fluent in one with good proficiency in the other national language.
ENG: English is considered a strong plus.