Senior AI Engineer JP053929

About the job Senior AI Engineer JP053929****Senior AI Engineer – RAG / Azure AI Platform (Consultancy Assignment) Role Overview

We are looking for a Senior AI Engineer to join the Digital Innovation / AI Center of Excellence team during the Build and early Run phases of the service. You will work closely with the AI Platform Architect and Solution Architect to transform the high-level design into a production-ready, scalable, and maintainable system.

Responsibilities

You will be responsible for end-to-end implementation of the AI search solution, including:

• Design and implementation of a full RAG pipeline (retrieval, reranking, LLM orchestration, citation handling)
• Development of ingestion pipelines (CMS & document sources such as SharePoint), including chunking and embedding strategies
• Implementation of security trimming and access control propagation (ACL-based filtering)
• Design of prompt templates, guardrails, and safety mechanisms
• Setup of evaluation frameworks to measure search and LLM performance
• Infrastructure-as-Code using Terraform for Azure resources
• Deployment automation using Azure DevOps pipelines and Helm charts on AKS
• Operational tuning and optimization during early production run

Tech Environment

• Azure OpenAI, Azure API Management, Azure AI Content Safety
• Kubernetes (AKS)
• Vector databases / search index (open-source/self-hosted)
• Python-based AI/ML pipelines
• Terraform, Helm, Azure DevOps
• RAG frameworks and LLM orchestration tooling

Engagement Model

• Assignment via consultancy (freelancer or contractor via our organization)
• Hybrid enterprise environment with strong engineering ownership
• Long-term engagement (initial period with possible extension up to ~3+ years equivalent scope)

Screening Questions

• Have you personally built or led the implementation of a Retrieval-Augmented Generation (RAG) system that went to production?
• If yes: please describe the RAG system briefly (business domain, approximate document corpus size, retrieval stack used including embeddings, vector store, reranking if any, and retrieval quality metrics). (max 300 words)
• Have you personally authored Kubernetes manifests or Helm charts and Terraform code for Azure as part of deploying an AI or ML workload to production?
• If yes: please provide one concrete example where you were personally responsible for both Kubernetes (manifests or Helm charts) and Terraform. (max 200 words)
• Have you designed and implemented an evaluation framework (metrics, test sets, automation) for a RAG or LLM application you worked on?
• If yes: please describe the evaluation methodology, including metrics used, test set creation, and how evaluation was integrated into the deployment or iteration cycle. (max 200 words)
• This role requires on-site presence in Brussels 2 to 3 days per week. Are you willing and able to commit to this schedule?