Artificial Intelligence Engineer

Senior Software Engineer – AI Applications (LLMs, RAG & Agentic AI)

Industry: Utilities

Location: Brussels, Belgium

Work Model: Hybrid

About the Role

Join a leading organization in the Energy & Utilities sector developing next-generation AI-centric applications. You will work within a software engineering team building production-grade AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI assistants, and agentic workflows.

This is a hands-on engineering role where you will help design, develop, evaluate, and optimize AI-powered applications that are secure, scalable, maintainable, and ready for production.

Key Responsibilities

• Design and implement AI-powered applications using LLMs, RAG, and agentic workflow patterns
• Build backend services, APIs, and integrations supporting AI applications
• Design and optimize retrieval pipelines including:
• Chunking
• Embeddings
• Vector search
• Hybrid search
• Metadata filtering
• Reranking
• Work with LLM APIs and AI orchestration frameworks
• Implement evaluation, testing, monitoring, and observability for AI applications
• Define safe and practical patterns for:
• Tool use
• Human-in-the-loop approval
• Agentic behaviour
• Collaborate with software engineers, product teams, and business stakeholders
• Support AI model and framework selection based on:
• Quality
• Cost
• Latency
• Maintainability
• Security
• Troubleshoot AI challenges including:
• Hallucinations
• Retrieval quality
• Latency
• Cost optimisation
• Output reliability

Required Skills & Experience

• Fluent in English and French or Dutch
• Minimum 5 years of Software Engineering experience (Backend or Full Stack preferred)
• 1–2 years of experience integrating LLMs or Generative AI into production software
• Strong experience with:
• Python
• RAG
• Embeddings
• Vector Search
• Retrieval optimisation
• Experience with:
• MCP
• A2A
• Tool Calling
• Multi-Agent Workflows
• Experience building maintainable services with:
• Testing
• Logging
• CI/CD
• Deployment
• Knowledge of AI evaluation methodologies including:
• Test datasets
• Quality metrics
• Regression testing
• User feedback
• Good understanding of cloud-native application development
• Security-focused mindset when working with sensitive data
• Strong communication skills with the ability to explain technical trade-offs
• Ability to determine when AI is the right solution versus deterministic approaches

Nice to Have

• LangGraph
• LangChain
• Semantic Kernel
• AI observability and evaluation tools
• Azure Cloud
• Microsoft AI ecosystem:
• Semantic Kernel
• Microsoft Agent Framework
• Microsoft Foundry
• Microsoft 365 Agents SDK
• .NET / C#
• Vector databases
• Enterprise search platforms
• Experience in regulated or security-sensitive enterprise environments