Description
ProUnity est le point de contact unique (SPOC) pour cette mission. Si vous avez des questions, vous pouvez contacter Henri Couchard, MSP Consultant, par e-mail à paradigm@pro-unity.com
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Job Description
Context
STIB-MIVB (Brussels public transport operator) is building several employee- and customer-facing AI services, serving our customers and employees across the organization. The AI services are build and ran on our STIB AI Platform.
Your role
We are recruiting a Senior AI Engineer to join the Digital Innovation / AI CoE team for the Build and hypercare phases of some our most complex AI services. You will work alongside STIB’s internal AI Platform Architect, the other Senior AI Engineer (already on board), and the project teams (project manager, business analyst, solution architect), turning our AI projects into running, evaluated and maintainable services.
The AI Platform runs on a hybrid operating model: managed Azure services (Azure AI Foundry with the OpenAI model family for LLM and embeddings, Azure AI Search, Azure API Management, Azure AI Content Safety) combined with self-hosted open-source components on AKS (agent orchestration in Python with LangChain, ingestion pipeline), with MLflow on Databricks for evaluation and tracing. The Digital Innovation / AI CoE team owns Infrastructure-as-Code for all application-scoped Azure resources, using Terraform and Azure DevOps pipelines.
What you will deliver
Activities will vary from project to project. Typically you will deliver:
- the RAG pipeline end-to-end, from retrieval strategy to answer generation ;
- the ingestion pipeline: connectors to source platforms, chunking, embedding, incremental refresh ;
- security trimming and ACL propagation ;
- agent orchestration, prompt templates and guardrails ;
- the evaluation and tracing setup: offline evaluation sets, retrieval and answer-quality metrics, end-to-end tracing of requests and agent steps ;
- the Terraform, Helm and Azure DevOps artefacts needed to deploy and operate the service ;
- Observability and cost control: instrumentation for latency, token consumption and run cost, plus quality regression detection after model or prompt changes ;
- Hypercare and early production: incident analysis, tuning, runbooks and documentation, and handover of the service to the engineer who will own the Run phase.
