MLOps Engineer
Il y a 4 heures
Brussels, Belgique
develop
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pDevelop are looking for an experienced bSenior MLOps Engineer /b to support the development, deployment and optimisation of production-grade Machine Learning infrastructure within a large-scale enterprise environment. /p pThis is a hands-on engineering role focused on building scalable ML platforms, automating model deployment and improving the reliability and performance of machine learning systems. /p h3Key
Responsibilities:
/h3 ul liDesign, build and maintain scalable MLOps pipelines and infrastructure. /li liDevelop automated CI/CD pipelines for machine learning model training, testing and deployment. /li liDeploy and manage ML models in production environments. /li liImplement model monitoring, versioning, experiment tracking and performance optimisation. /li liBuild and maintain containerised ML workloads using Docker and Kubernetes. /li liWork closely with Data Scientists, ML Engineers and Platform Engineering teams. /li liImplement Infrastructure as Code (IaC) and automated cloud provisioning. /li liImprove model lifecycle management, observability and platform reliability. /li /ul h3Essential Technical
Skills:
/h3 ul liStrong commercial experience in MLOps or ML Platform Engineering. /li liAdvanced Python programming skills. /li liHands‑on experience with bMLflow, Kubeflow or similar MLOps frameworks /b. /li liStrong knowledge of bDocker and Kubernetes /b. /li liExperience building bCI/CD pipelines using GitHub Actions, GitLab CI or Azure DevOps /b. /li liCloud experience with bAWS SageMaker, Azure Machine Learning or Google Vertex AI /b. /li liInfrastructure as Code using bTerraform /b. /li liExperience with model serving, monitoring and automated retraining pipelines. /li liUnderstanding of ML model lifecycle management, feature stores and data pipelines. /li liLLMOps and deployment of Generative AI models. /li liModel serving frameworks such as KServe, BentoML or NVIDIA Triton. /li liDistributed model training and inference. /li liMonitoring tools such as Prometheus and Grafana. /li /ul
Responsibilities:
/h3 ul liDesign, build and maintain scalable MLOps pipelines and infrastructure. /li liDevelop automated CI/CD pipelines for machine learning model training, testing and deployment. /li liDeploy and manage ML models in production environments. /li liImplement model monitoring, versioning, experiment tracking and performance optimisation. /li liBuild and maintain containerised ML workloads using Docker and Kubernetes. /li liWork closely with Data Scientists, ML Engineers and Platform Engineering teams. /li liImplement Infrastructure as Code (IaC) and automated cloud provisioning. /li liImprove model lifecycle management, observability and platform reliability. /li /ul h3Essential Technical
Skills:
/h3 ul liStrong commercial experience in MLOps or ML Platform Engineering. /li liAdvanced Python programming skills. /li liHands‑on experience with bMLflow, Kubeflow or similar MLOps frameworks /b. /li liStrong knowledge of bDocker and Kubernetes /b. /li liExperience building bCI/CD pipelines using GitHub Actions, GitLab CI or Azure DevOps /b. /li liCloud experience with bAWS SageMaker, Azure Machine Learning or Google Vertex AI /b. /li liInfrastructure as Code using bTerraform /b. /li liExperience with model serving, monitoring and automated retraining pipelines. /li liUnderstanding of ML model lifecycle management, feature stores and data pipelines. /li liLLMOps and deployment of Generative AI models. /li liModel serving frameworks such as KServe, BentoML or NVIDIA Triton. /li liDistributed model training and inference. /li liMonitoring tools such as Prometheus and Grafana. /li /ul