AI Specialist (Junior - Medior)
HybridBrussels, Brussels Capital, Belgium
Job Summary
Analyse, develop, test, and deploy AI-driven solutions, proof of concepts, pilot projects, and production-ready applications. Contribute to the evolution and production rollout of enterprise AI platforms, chatbots, and related AI services. Support automation and optimisation of document management, legal document tagging, and knowledge retrieval using AI technologies. Develop Generative AI solutions leveraging Large Language Models, embeddings, vector databases, and AI APIs. Collaborate with stakeholders to translate functional requirements into scalable AI solutions and prepare technical documentation and architecture deliverables. Participate in AI solution deployment, operationalisation, scaling, monitoring, and production support activities. Contribute to AI standards, best practices, and continuous improvement within the ecosystem. Work closely with multidisciplinary teams in an agile environment.
Required Qualifications
- University degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field
- Experience with programming languages used in data analytics and Artificial Intelligence, particularly Python and Java
- Knowledge of prompt engineering techniques, including prompt design, evaluation, optimisation, and analysis of model behaviour
- Hands-on experience with Generative AI technologies, Large Language Models, embeddings, vector databases, open-source LLMs, and AI APIs
- Understanding of AI solution architecture, containerisation, private cloud environments, CI/CD practices, and MLOps methodologies
- Strong analytical and problem-solving skills with the ability to troubleshoot complex technical challenges and assess evolving technology trends
- Excellent communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders in English
Desired Qualifications
- knowledge of French is considered an asset
- Additional experience with SQL, ETL/ELT pipelines, data modelling, orchestration tools, data visualisation platforms, business case analysis, and maturity assessments would be considered an asset
- Experience with AI frameworks and technologies such as FastAPI, Spring Boot, Flask, vector databases (e.g. Qdrant), and document processing solutions is considered an asset
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