EngiFlex
Skills: Python, Docker
π Tasks and responsibilities
For a public transport organisation in Brussels, an AI Centre of Excellence is building several AI services for employees and customers. These include retrieval-based assistants and AI agents that call tools and business systems on behalf of users. The services run on a dedicated AI platform. The first services are scheduled to go live from December 2026, with further services planned for 2027.
Your role
As a Medior AI Engineer, you take ownership of the Run phase for AI services in production. You take over each service from the senior engineers who built it and keep it reliable, measurable, secure and cost-efficient over time.
You work with an AI platform architect, two senior AI engineers responsible for the Build phase, the business and content owners of each service, and the IT operations teams.
The platform follows a hybrid operating model. It combines managed Azure services through Microsoft Foundry and Azure OpenAI Service for LLMs and embeddings, Azure AI Search, Azure API Management and Azure AI Content Safety with self-hosted open-source components on Azure Kubernetes Service (AKS). These components include Python-based agent orchestration with LangChain and an ingestion pipeline. MLflow on Databricks is used for evaluation and tracing with OpenTelemetry. Infrastructure-as-Code for application-scoped Azure resources is managed with Terraform and Azure DevOps pipelines.
What you will deliver
Your activities will vary from service to service. Typical responsibilities include:
Operate AI services in production by monitoring availability, latency and errors, handling incidents and service requests, analysing root causes, and fixing or escalating issues. Maintain answer quality by running evaluation sets, or golden sets, with business content owners. Track answer quality, non-answer rates and user feedback, and investigate quality regressions. Operate ingestion pipelines, including incremental refreshes, failed documents, index health, andβ¦
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