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

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