Context

STIB-MIVB (Brussels public transport operator) is building several employee- and customer-facing AI services, serving our customers and employees across the organization. These range from retrieval-based assistants to AI agents that call tools and business systems on behalf of users. The AI services are built and run on our STIB AI Platform. The first services go live from December 2026, and more will follow in 2027.

Your role

We are recruiting a Medior AI Engineer to join the AI Center of Excellence (Digital Innovation & AI – delivery team) and own the Run phase of our AI services in production. You take over each service from the Senior AI Engineers who built it, and you keep it reliable, measured, secure and cost-efficient over time. You will work alongside STIB’s internal AI Platform Architect, two Senior AI Engineers (Build), the business and content owners of each service, and STIB’s IT operations teams.

The AI Platform runs on a hybrid operating model: managed Azure services (Microsoft 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 (OpenTelemetry). 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 service to service. Typically you will:

• operate the AI services in production: monitor availability, latency and errors, handle incidents and service requests, analyse root causes, then fix or escalate
• keep answer quality under control: run and maintain evaluation sets (golden sets) with business content owners, track answer quality, non-answer rate and user feedback, and detect and investigate quality regressions
• operate the ingestion pipelines: incremental refresh, failed documents, index health, and onboarding of new content sources or business domains within existing connectors
• operate agent integrations: monitor tool and integration calls to business systems, handle failures and timeouts, and review action logs and exceptions for agents that act on users’ behalf
• manage change safely: model version upgrades and retirements, prompt and configuration changes, dependency and security updates, each validated against the evaluation sets before release
• follow run cost: token consumption and cost per service, alerting on drift and proposing optimisations
• maintain the Terraform, Helm and Azure DevOps artefacts, and keep the DEV, TST, ACC and PROD environments consistent
• apply data protection rules: trace retention, erasure and access restrictions, as defined in each service’s data protection impact assessment
• maintain runbooks and documentation, and report service health to service owners and the AI CoE on a regular basis
• deliver small enhancements and fixes; structural changes and new features stay with the Build team.