Software Engineer

Xintas; Brussel Hoofdstad; Hybride; EUR 70 000 - 95 000; Plein temps

Résumé du poste

Xintas is seeking a Software Engineer to join AI-centric software teams focused on building reliable, scalable AI-powered applications. The role blends strong software engineering with practical experience in LLMs, RAG, and agentic workflows to deliver production‑quality solutions.

You will design, implement, and evaluate AI systems, build back‑end services and APIs, and collaborate with product and security stakeholders while applying robust testing, CI/CD, logging, and deployment practices.

Qualifications

• Minimum 5 years of experience as Software Engineer, backend or full-stack.
• 1–2 years of experience integrating LLMs or other generative AI services into software applications.
• Strong Python programming and cloud-native development.
• Fluent English plus French or Dutch.

Responsabilités

• Design and implement AI-powered applications using LLMs, RAG, and agentic workflows.
• Build backend services, APIs, and integrations for AI-centric apps.
• Design and improve retrieval pipelines with embeddings, vector search, and hybrid search.
• Work with LLM APIs and AI orchestration frameworks for reliable workflows.
• Implement evaluation, testing, monitoring, and observability for AI applications.
• Define safe patterns for tool use and human-in-the-loop approvals.
• Collaborate with engineers and product stakeholders.
• Support model and framework selection focusing on quality, cost, latency, security.
• Troubleshoot issues such as hallucinations, retrieval quality, latency, and cost.

Connaissances

Python

Backend development

Full-stack development

AI integration

LLMs / RAG

CI/CD

Testing / observability

Security mindset

English language

French or Dutch

Outils

LangGraph

LangChain

Semantic Kernel

Azure

Microsoft AI tools

.NET / C#

Vector databases

Description du poste

We are looking for a Software Engineer to join one of our software engineering teams focused on developing AI-centric applications.

The role combines strong software engineering skills with practical experience in building solutions using LLMs, RAG, AI assistants, and agentic workflows. The ideal candidate can help design, implement, evaluate, and improve AI-powered applications that are reliable, secure, maintainable, and useful in a real business environment.

This is a hands‑on engineering role. We are looking for someone who can build production‑quality solutions, contribute to technical direction, and support the team in applying AI engineering practices effectively.

Tasks

• Design and implement AI-powered applications using LLMs, RAG, and agentic workflow patterns.
• Build backend services, APIs, and integrations needed for AI-centric applications.
• Design and improve retrieval pipelines, including chunking, embeddings, vector search, hybrid search, metadata filtering, and reranking.
• Work with LLM APIs and AI orchestration frameworks to build reliable workflows.
• Implement evaluation, testing, monitoring, and observability for AI applications.
• Help define safe and practical patterns for tool use, human‑in‑the‑loop approval, and agentic behavior.
• Collaborate with other engineers and product/business stakeholders.
• Support model and framework selection based on quality, cost, latency, maintainability, and security.
• Help troubleshoot issues such as hallucinations, poor retrieval quality, high latency, cost inefficiency, and unreliable outputs.

Ideal Profile

• You are able to speak, read and write fluently English and French or Dutch.
• Minimum 5 years of experience as Software Engineer, preferably in backend or full-stack development.
• Minimum 1 to 2 years of experience integrating LLMs or other generative AI services into software applications.
• Practical knowledge of RAG, embeddings, vector search, and retrieval quality improvement.
• Strong programming skills in Python.
• Experience with MCP, A2A, tool calling, or multi-agent workflows.
• Experience designing maintainable services with proper testing, logging, CI/CD, and deployment practices.
• Understanding of AI application evaluation, including test datasets, quality metrics, regression testing, and user feedback.
• Good understanding of cloud-native application development.
• Security‑conscious mindset when working with internal or sensitive data.
• Ability to communicate technical trade‑offs clearly and pragmatically.
• Ability to distinguish when AI, RAG, or agents are useful and when a simpler deterministic solution is better.

Nice to Have

• Experience with AI frameworks such as LangGraph, LangChain, Semantic Kernel.
• Experience with observability or evaluation tools.
• Familiarity with Azure cloud environment.
• Experience with Microsoft AI ecosystem tools such as Semantic Kernel, Microsoft Agent Framework, Microsoft Foundry, or Microsoft 365 Agents SDK.
• Experience with .NET / C#.
• Experience with vector databases or enterprise search platforms.
• Experience working in regulated, security-sensitive, or enterprise environments.

The right consultant should be comfortable working across software engineering, AI integration, evaluation, security, and production readiness. They should be able to move fast when building MVPs, while still applying enough engineering discipline to make successful solutions scalable and maintainable.