Satellit - AI Software Engineer Service
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Summary
Satellit is hiring a software engineer in Belgium to build AI-first business applications: architecting LLM, RAG and agent-driven workflows with backend services, APIs and document retrieval, plus orchestration, testing, CI/CD, logging and monitoring. Core stack: Python and C#/.NET on Azure with LangChain, LangGraph, Semantic Kernel and Microsoft Foundry/Agents SDK.
Satellit is seeking a software engineer to join a development group building AI-first applications. The role blends solid software engineering practice with hands-on experience creating systems that use large language models, retrieval-augmented generation, conversational assistants and agent-driven workflows. The appointed engineer will architect, build, validate and iterate on AI-enabled software so it operates securely, stays maintainable, and delivers dependable value in a production business setting.
Who are you?
Skills & Competences:
- Analytical decision-making for AI versus deterministic solutions
- Cloud-native application development
- AI application evaluation methods including test datasets, quality metrics, regression testing, and user feedback
- Technical communication and trade-off analysis
- Collaboration and stakeholder management
- Service design with testing, logging, CI/CD, and deployment practices
- Security-conscious mindset when working with internal or sensitive data
Preferred Qualifications:
- At least five years of professional experience as a full‑stack developer.
What will you do?
- Architect and build AI-driven applications that combine large language models, retrieval-augmented generation, agent-style workflows, backend services, APIs and document retrieval pipelines.
- Implement and sustain robust orchestration for LLM integrations, including automated tests, centralized logging, CI/CD pipelines, and runtime monitoring to ensure observability.
- Establish safe, practical patterns for tooling, human approval gates, and controlled agent behavior; advise on model and framework choices with attention to accuracy, cost, latency, maintainability and security.
- Diagnose and resolve common AI system problems such as model hallucinations, poor retrieval relevance, excessive response times, ineffective cost profiles and unstable outputs.
- Work closely with software engineers and product or business stakeholders to embed AI engineering best practices and help shape the technical roadmap.
Technologies (mandatory)
- MS Teams
- advanced llm-based ai generation
- RAG Pipeline
- Microsoft 365 Agents SDK
- SAFe Agile
- Logging
- Langgraph
- Langchain
- AI
- Semantic Kernel
- Security
- Microsoft Foundry
- Azure Cloud Platform
- Python
- C#
- product owner
- .NET / C#
- Microsoft Agent Framework
- Innovation
- Collaboration
- Azure Cloud
- Vector Database Or Enterprise Search Platform