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

1. 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)
2. 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)
3. 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)
4. This role requires on-site presence in Brussels 2 to 3 days per week. Are you willing and able to commit to this schedule?