LLM Ops Engineer — Build Scalable ML Pipelines

Meerab Group; Brussel Hoofdstad; On-site; EUR 65,000 - 90,000; Full time

Job summary

Meerab Group in Belgium is seeking an experienced ML Ops/Infrastructure Engineer to design, build, and operate production-grade ML systems centered on LLMs across hybrid on-prem and cloud infrastructure.

You will deploy, test, monitor, and manage models in containerized environments, develop the LLMOps lifecycle (data prep, fine-tuning, evaluation, deployment, monitoring), and create CI/CD pipelines and data tooling. Strong Python, Kubernetes, and cloud experience are required.

Qualifications

• Experience managing on-premise and/or cloud LLM/MLOps infra.
• Proficiency with containerisation and orchestration platforms like Kubernetes, Docker, Podman, EKS, PKS.
• Hands-on with MLflow, TensorFlow (TFX), Airflow and major cloud providers (AWS or EU cloud).
• Strong Python, Unix/Bash, and IaC tools (Terraform or CloudFormation).
• Familiarity with Kafka, Redis, RabbitMQ and NoSQL DBs (OpenSearch, MongoDB).

Responsibilities

• Design, build, and maintain production-grade ML systems centering on LLMs across hybrid on-prem/cloud infrastructure.
• Deploy, test, monitor, and manage ML models in containerised environments.
• Develop and evolve the LLMOps lifecycle, including data preparation, fine-tuning, evaluation, deployment, and monitoring.
• Create CI/CD pipelines, orchestration solutions, and data versioning tools for ML workflows.
• Implement automated anomaly detection, performance tracking, and cost-optimisation for ML pipelines.

Tools

Kubernetes

Docker

Podman

EKS

PKS

Terraform

CloudFormation

Kafka

Redis

RabbitMQ

OpenSearch

MongoDB

MLflow

TensorFlow

Airflow

Job description

Meerab Group in Belgium is seeking an experienced ML Ops/Infrastructure Engineer to design, build, and operate production-grade ML systems centered on LLMs across hybrid on-prem and cloud infrastructure.

You will deploy, test, monitor, and manage models in containerized environments, develop the LLMOps lifecycle (data prep, fine-tuning, evaluation, deployment, monitoring), and create CI/CD pipelines and data tooling. Strong Python, Kubernetes, and cloud experience are required.