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AI Engineer - STIB-MIVB

Customer: STIB-MIVB Rue Royale 76 1000 Bruxelles Belgium
Dates: 2027-01-04 — 2028-01-03
Arrangements: Full time; hybrid; on-site in Brussels 2 to 3 days per week.
Apply before: 2026-11-15
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Description

STIB-MIVB is building employee- and customer-facing AI services across the organization. Services range from retrieval-based assistants to AI agents that call tools and business systems on users’ behalf. They run on the STIB AI Platform. The first services go live from December 2026, with more following in 2027.

The Medior AI Engineer joins the AI Center of Excellence’s Digital Innovation & AI delivery team and owns production operations. Each service is handed over by the Senior AI Engineers who built it. The engineer keeps services reliable, measured, secure and cost-efficient. Collaboration includes the internal AI Platform Architect, two Senior AI Engineers responsible for Build, business and content owners, and IT operations teams.

The platform combines managed Azure services with self-hosted open-source components on AKS. Microsoft Foundry uses the OpenAI model family for LLMs and embeddings, alongside Azure AI Search, Azure API Management and Azure AI Content Safety. Python and LangChain support agent orchestration and an ingestion pipeline, with MLflow on Databricks for evaluation and OpenTelemetry tracing. The team owns application-scoped Azure Infrastructure-as-Code through Terraform and Azure DevOps pipelines; small enhancements belong to Run, while structural changes and new features stay with Build.

Top Reasons to Apply
Production AI ownership
★★★★★
Agents and RAG
★★★★★
Hybrid Azure platform
★★★★★
Public transport impact
★★★★★
Cross-functional AI collaboration
★★★★★
Responsibilities
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 ;
Must Have
Medior AI engineering experience
Azure DevOps intermediate proficiency
Azure Kubernetes Service - AKS intermediate proficiency
Azure OpenAI Service intermediate proficiency
LangChain intermediate proficiency
LLM / Large Language Models intermediate proficiency
Python intermediate proficiency
RAG (Retrieval-Augmented Generation) intermediate proficiency

Detailed Responsibilities and Skills

Additional Responsibilities

  • 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.

General skills

  • Medior Artificial Intelligence (AI) Engineer; most recent experience: this year (2026).
  • Dutch or French: native or bilingual proficiency (one of the two languages).
  • English: full professional proficiency.
  • On-site presence in Brussels 2 to 3 days per week.

Technical skills and tools

  • Azure DevOps: intermediate level; most recent experience: this year (2026).
  • Azure Kubernetes Service - AKS: intermediate level; most recent experience: this year (2026).
  • Azure OpenAI Service: intermediate level; most recent experience: this year (2026).
  • LangChain: intermediate level; most recent experience: any time.
  • LLM / Large Language Models: intermediate level; most recent experience: this year (2026).
  • Python: intermediate level; most recent experience: this year (2026).
  • RAG (Retrieval-Augmented Generation): intermediate level; most recent experience: this year (2026).
  • Terraform: intermediate level; most recent experience: this year (2026).
  • Vector databases: intermediate level; most recent experience: this year (2026).

Selection questions — status not stated as mandatory

  • Question 1 screens for at least 6 months personally operating or supporting a production LLM application (RAG, agent or document processing).
  • Question 2 asks for personal investigation of an LLM or agent quality issue or incident using traces or logs.
  • Question 3 asks for running or maintaining LLM evaluations, including test sets after model or prompt changes.
  • Question 4 asks for personal deployment of Kubernetes manifests or Helm charts and Terraform code through Azure CI/CD pipelines.