Artificial Intelligence Engineer ( Python)

La Fosse

Brussels Metropolitan Area, Belgium

6 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.adzuna.be

Tech stack

Clean Code Principles Artificial Intelligence Airflow Amazon Web Services Automation of Tests Microsoft Azure Big Data Cloud Engineering Continuous Integration Github Python (Programming Language) Machine Learning

+21 more

Open Source Technology Performance Tuning Azure Machine Learning Software Engineering SQL Databases Workflow Management Systems Data Logging Feature Engineering DevOps Tools - Open-source Large Language Models Model Validation Generative AI Pytest Gitlab-ci Git Flow Kubernetes Machine Learning Operations Virtual Agents Asynchronous Programming Software Version Control Docker

Requirements

Advanced proficiency in Python (including typing, packaging, asynchronous programming, and performance optimisation) and SQL, with experience writing and optimising complex production queries, window functions, and large-scale data joins.
Hands-on experience with at least two key Generative AI components, including:Retrieval-Augmented Generation (RAG) solutions, covering chunking strategies, hybrid search, and result re-ranking.
Agentic AI workflows using frameworks such as LangGraph, CrewAI, AutoGen, or custom-built orchestration frameworks.
Model fine-tuning and PEFT techniques, including LoRA and QLoRA, for both open-source and proprietary models.
Deployment and management of vector databases in production environments, such as Pinecone, Weaviate, Qdrant, pgvector, or FAISS.
Experience with LLM orchestration frameworks including LangChain, LlamaIndex, Semantic Kernel, or similar technologies.
Strong foundation in traditional Machine Learning, including feature engineering, model selection, experimentation, and model evaluation.
Proven cloud engineering experience across AWS, Azure, or GCP, including the deployment, scaling, governance, and cost optimisation of AI/ML workloads using services such as SageMaker, Azure ML, or Vertex AI.
Solid software engineering practices, including clean coding standards, modular architecture, automated testing (pytest/unittest), version control, and collaborative Git workflows.
Experience with MLOps and DevOps tooling, including Docker (essential), alongside at least one of the following:CI/CD platforms (GitHub Actions, GitLab CI, Azure DevOps)
Workflow orchestration tools (Airflow, Prefect, Dagster)
Container orchestration platforms (Kubernetes)
Familiarity with AI monitoring and observability tooling such as Langfuse, Arize, MLflow, Weights & Biases, or custom evaluation and logging frameworks to ensure reliability and performance in production environments.