MLOps LLMOps Engineer,Kubernetes & MLflow

We are looking for an MLOps / LLMOps Engineer for an international organization in Brussels Belgium with strong expertise in Machine Learning Operations LLM infrastructure Kubernetes and MLflow.

You will manage and optimize MLOps and Large Language Model (LLM) infrastructure across on-premises and/or cloud environments supporting scalable ML deployment model monitoring automation and production reliability.

Candidates need to be based in Europe within 2 hours of Brussels. Consultants will need to go 5 days per month on-site. European candidates are welcome to apply but need to relocate to Brussels or within 2 hours of Brussels.

A work permit is required but not provided.

Tasks and Responsibilities

• Manage and maintain MLOps and LLMOps infrastructure across on-premises and/or cloud environments;
  
• Support the deployment orchestration and operation of ML and LLM workloads using Kubernetes Docker or Podman;
  
• Develop and maintain ML pipelines and workflows using MLflow TensorFlow Extended (TFX) Airflow or equivalent technologies;
  
• Automate infrastructure provisioning and configuration using Terraform or AWS CloudFormation;
  
• Implement monitoring logging and observability using tools such as Prometheus Grafana ELK Stack and OpenTelemetry;
  
• Support model testing validation and performance monitoring in production environments;
  
• Work with data pipelines messaging systems NoSQL databases and large-scale data environments;
  
• Apply security practices support Agile delivery and maintain clear technical documentation.
  

Profile

• Bachelors or Masters degree;
  
• 6 years of IT experience including 3 years of MLOps experience;
  
• Hands-on experience managing on-premises and/or cloud-based MLOps or LLM infrastructure;
  
• Strong knowledge of Kubernetes Docker or Podman including container orchestration environments such as EKS or PKS;
  
• Experience with MLflow TensorFlow Extended (TFX) or equivalent MLOps tools and Apache Airflow;
  
• Experience with AWS or European cloud providers;
  
• Proficiency in Python Unix and Bash scripting;
  
• Experience with Infrastructure as Code (IaC) using Terraform or CloudFormation;
  
• Knowledge of messaging technologies such as Kafka Redis or RabbitMQ NoSQL databases and query languages including SQL Hive or Pig;
  
• Experience with large-scale data analytics data lakes production model validation monitoring tools Agile methodologies and technical documentation.
  
• Knowledge of data encryption and ML security practices would be an advantage.
  
• Fluent English is required.
  

Key Technologies & Skills

MLOps LLMOps Machine Learning Operations Kubernetes Docker MLflow TensorFlow Extended Apache Airflow Python AWS Terraform Infrastructure as Code Prometheus Grafana Model Monitoring