Medior AI Engineer – LLMOps
Il y a 5 heures
Arrondissement of BrusselsCapital, Brussels, Belgique
HumanInTech
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h3What you will do /h3 pA Belgian public transport company is building employee
- and customer-facing AI services on an AI platform running on Azure and Kubernetes. The first services go live in December 2026, with more to follow in 2027. You will join the AI Centre of Excellence and own the Run phase of these AI services in production. /p pYou will take over each service from the Senior AI Engineers who built it and keep it reliable, measured, secure and cost-efficient over time. You will work alongside the internal AI Platform Architect, two Senior AI Engineers, the business and content owners of each service, and the IT operations teams. /p pThe AI Platform runs on a hybrid operating model: managed Azure services (Azure OpenAI Service, Azure AI Search, Azure API Management, Azure AI Content Safety) combined with self-hosted open-source components on Azure Kubernetes Service (agent orchestration in Python with LangChain, ingestion pipeline), with MLflow on Databricks for evaluation and tracing (OpenTelemetry). The AI Centre of Excellence team owns Infrastructure-as-Code for all application-scoped Azure resources, using Terraform and Azure DevOps pipelines. /p pYour activities will vary from service to service. Typicaly you will: /p ul liOperate the AI services in production: monitor availability, latency and errors, handle incidents and service requests, analyse root causes, then fix or elevate /li liKeep 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 /li liOperate the ingestion pipelines: incremental refresh, failed documents, index health, and onboarding of new content sources or business domains within existing connectors /li liOperate 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 /li liManage change safely: model version upgrades and retirements, prompt and configuration changes, dependency and security updates, each validated against the evaluation sets before release /li liFollow run cost: token consumption and cost per service, alerting on drift and proposing optimisations /li liMaintain the Terraform, Helm and Azure DevOps artefacts, and keep the DEV, TST, ACC and PROD environments consistent /li liApply data protection rules: trace retention, erasure and access restrictions, as defined in each service's data protection impact assessment /li liMaintain runbooks and documentation, and report service health to service owners and the AI Centre of Excellence on a regular basis /li liDeliver small enhancements and fixes; structural changes and new features stay with the Build team /li /ul h3What we are looking for /h3 pYou have intermediate-level experience with the following technologies, all gained this year or recently: /p ul liAzure DevOps /li liAzure Kubernetes Service (AKS) /li liAzure OpenAI Service /li liLangChain /li liLLM / Large Language Models /li liPython /li liRAG (Retrieval-Augmented Generation) /li liTerraform /li liVector databases /li /ul pYou must be able to answer yes to the following questions and provide detailed examples: /p ul liHave you personally operated or supported an LLM application (RAG, agent or document processing) in production for at least 6 months? /li liHave you personally investigated a quality issue or incident in an LLM application or AI agent using traces or logs? /li liHave you run or maintained evaluations of an LLM application, for example re-running test sets after a model or prompt change? /li liHave you personally deployed changes to Kubernetes workloads (manifests or Helm charts) and Terraform code through CI/CD pipelines on Azure? /li /ul pYou are fluent in English and native or bilingual in Dutch or French. /p h3The setting /h3 pThis assignment runs for 12 months, starting on 4 January 2027. The work is hybrid, with on-site presence in Brussels required 2 to 3 days per week. /p pYou can join us for this assignment as a freelancer or as an employee of HumanInTech. Same role, same team. If you join as an employee, your employment continues beyond this assignment. When it ends, we’ll work together to find your next assignment. /p pbLocation: /b Brussels, hybrid /p pbWork address: /b Belgium /p pbEmployer / contracting party: /b HumanInTech /p pbApplications close (Brussels time): /b November 16, 2026 at 1:00 AM /p pbEngagement: /b Freelance or employed by HumanInTech /p pbWorking hours: /b Full-time /p pbExperience: /b 3–5 years or more /p pbEducation: /b Bachelor's or more /p pbPublished: /b November 2026 /p
- and customer-facing AI services on an AI platform running on Azure and Kubernetes. The first services go live in December 2026, with more to follow in 2027. You will join the AI Centre of Excellence and own the Run phase of these AI services in production. /p pYou will take over each service from the Senior AI Engineers who built it and keep it reliable, measured, secure and cost-efficient over time. You will work alongside the internal AI Platform Architect, two Senior AI Engineers, the business and content owners of each service, and the IT operations teams. /p pThe AI Platform runs on a hybrid operating model: managed Azure services (Azure OpenAI Service, Azure AI Search, Azure API Management, Azure AI Content Safety) combined with self-hosted open-source components on Azure Kubernetes Service (agent orchestration in Python with LangChain, ingestion pipeline), with MLflow on Databricks for evaluation and tracing (OpenTelemetry). The AI Centre of Excellence team owns Infrastructure-as-Code for all application-scoped Azure resources, using Terraform and Azure DevOps pipelines. /p pYour activities will vary from service to service. Typicaly you will: /p ul liOperate the AI services in production: monitor availability, latency and errors, handle incidents and service requests, analyse root causes, then fix or elevate /li liKeep 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 /li liOperate the ingestion pipelines: incremental refresh, failed documents, index health, and onboarding of new content sources or business domains within existing connectors /li liOperate 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 /li liManage change safely: model version upgrades and retirements, prompt and configuration changes, dependency and security updates, each validated against the evaluation sets before release /li liFollow run cost: token consumption and cost per service, alerting on drift and proposing optimisations /li liMaintain the Terraform, Helm and Azure DevOps artefacts, and keep the DEV, TST, ACC and PROD environments consistent /li liApply data protection rules: trace retention, erasure and access restrictions, as defined in each service's data protection impact assessment /li liMaintain runbooks and documentation, and report service health to service owners and the AI Centre of Excellence on a regular basis /li liDeliver small enhancements and fixes; structural changes and new features stay with the Build team /li /ul h3What we are looking for /h3 pYou have intermediate-level experience with the following technologies, all gained this year or recently: /p ul liAzure DevOps /li liAzure Kubernetes Service (AKS) /li liAzure OpenAI Service /li liLangChain /li liLLM / Large Language Models /li liPython /li liRAG (Retrieval-Augmented Generation) /li liTerraform /li liVector databases /li /ul pYou must be able to answer yes to the following questions and provide detailed examples: /p ul liHave you personally operated or supported an LLM application (RAG, agent or document processing) in production for at least 6 months? /li liHave you personally investigated a quality issue or incident in an LLM application or AI agent using traces or logs? /li liHave you run or maintained evaluations of an LLM application, for example re-running test sets after a model or prompt change? /li liHave you personally deployed changes to Kubernetes workloads (manifests or Helm charts) and Terraform code through CI/CD pipelines on Azure? /li /ul pYou are fluent in English and native or bilingual in Dutch or French. /p h3The setting /h3 pThis assignment runs for 12 months, starting on 4 January 2027. The work is hybrid, with on-site presence in Brussels required 2 to 3 days per week. /p pYou can join us for this assignment as a freelancer or as an employee of HumanInTech. Same role, same team. If you join as an employee, your employment continues beyond this assignment. When it ends, we’ll work together to find your next assignment. /p pbLocation: /b Brussels, hybrid /p pbWork address: /b Belgium /p pbEmployer / contracting party: /b HumanInTech /p pbApplications close (Brussels time): /b November 16, 2026 at 1:00 AM /p pbEngagement: /b Freelance or employed by HumanInTech /p pbWorking hours: /b Full-time /p pbExperience: /b 3–5 years or more /p pbEducation: /b Bachelor's or more /p pbPublished: /b November 2026 /p