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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Medior AI Engineer - **Company:** LACO - **Location:** Diegem (Machelen), Belgium - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Microsoft Azure, Continuous Integration, Infrastructure as a Service (IaaS), Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Azure Machine Learning, Azure Data Lake, Search Technologies, Data Ingestion, Azure Data Factory, Pytorch, Delivery Pipeline, Large Language Models, Generative AI, Scikit Learn, HuggingFace, Bicep, Azure AKS, Machine Learning Operations, Stable Diffusion, Terraform, GPT, Software Version Control, Data Pipelines, Docker - **Published:** September 18, 2026 - **Apply:** https://www.careerjet.be/jobad/be8c46f3d26e826341dbfe50f1221ca817 ## About the Role You have at least 2 years of experience as an AI or ML engineer, with hands-on project delivery You are proficient in Python and experienced with ML frameworks such as scikit-learn, TensorFlow and PyTorch, as well as open-source models like Hugging Face. You have worked with OpenAI's GPT-4 Turbo with Vision, Falcon, Stable Diffusion and Llama 2, using hybrid and semantic search to power retrieval-augmented generation (RAG) applications. Familiarity with other LLMs, including Mistral, Llama, and Gemma, is highly valued. You are comfortable to work within the Microsoft Azure ecosystem, including Azure Machine Learning, Azure Data Lake, Azure Data Factory, Azure DevOps, AKS and AI Foundry You apply MLOps practices, including version control, experiment tracking and pipeline automation You are familiar with CI/CD, Docker and IaaS (ARM, Bicep or Terraform) You analyse model performance and implement improvements You combine strong analytical thinking with a hands-on problem-solving mindset Your skills You have at least 2 years of experience as an AI or ML engineer, with hands-on project delivery You are proficient in Python and experienced with ML frameworks such as scikit-learn, TensorFlow and PyTorch, as well as open-source models like Hugging Face. You have worked with OpenAI's GPT-4 Turbo with Vision, Falcon, Stable Diffusion and Llama 2, using hybrid and semantic search to power retrieval-augmented generation (RAG) applications. Familiarity with other LLMs, including Mistral, Llama, and Gemma, is highly valued. You are comfortable to work within the Microsoft Azure ecosystem, including Azure Machine Learning, Azure Data Lake, Azure Data Factory, Azure DevOps, AKS and AI Foundry You apply MLOps practices, including version control, experiment tracking and pipeline automation You are familiar with CI/CD, Docker and IaaS (ARM, Bicep or Terraform) You analyse model performance and implement improvements You combine strong analytical thinking with a hands-on problem-solving mindset ## Description As our Medior AI Engineer, you will go beyond supporting projects. You will actively shape real-world AI in close collaboration with data scientists, data engineers and software developers. From designing and deploying scalable, reliable AI models and building pipelines to experimenting with Generative AI, you'll turn complex challenges into scalable solutions while growing your expertise every step of the way. Your job Support the development and implementation of AI solutions on our client's Azure platform Deploy machine learning models using Azure Machine Learning Studio Develop Generative AI applications, including AI copilots, using Azure AI Foundry Build data pipelines for data ingestion, training and deployment (CI/CD) of models Monitor, adapt and maintain AI solutions within a production environment Apply responsible AI principles such as fairness, explainability and privacy in every step Collaborate with stakeholders and teams to implement AI applications that deliver value Your job Support the development and implementation of AI solutions on our client's Azure platform Deploy machine learning models using Azure Machine Learning Studio Develop Generative AI applications, including AI copilots, using Azure AI Foundry Build data pipelines for data ingestion, training and deployment (CI/CD) of models Monitor, adapt and maintain AI solutions within a production environment Apply responsible AI principles such as fairness, explainability and privacy in every step Collaborate with stakeholders and teams to implement AI applications that deliver value ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Back(end) to the Future: Embracing the continuous Evolution of Infrastructure and Code](https://www.wearedevelopers.com/videos/440-back-end-to-the-future-embracing-the-continuous-evolution-of-infrastructure-and-code) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [From Traction to Production: Maturing your LLMOps step by step](https://www.wearedevelopers.com/videos/1250-from-traction-to-production-maturing-your-llmops-step-by-step) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)