MLOps / LLMOps Engineer Senior
Senior MLOps/LLMOps engineer in Brussels who designs, deploys, monitors and optimises production-grade ML and LLM systems in secure hybrid on-premise/cloud environments for EU institutions. Core stack: Kubernetes/Docker, MLflow, Airflow, AWS, Python, Terraform, Kafka and monitoring tools like Prometheus, Grafana and ELK.
- Compensation
- Not disclosed
- Location
- Brussels, Belgium
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Devops
- Posted
Skills
Must have
- Excellent knowledge of managing an on-prem and/or cloud LLM/MLOps infrastructure
- Excellent knowledge of containerisation and orchestration platforms (Kubernetes, Docker, Podman, EKS, PKS)
- Good knowledge of MLflow, TensorFlow (TFX) or equivalents
- Good knowledge of Airflow
- Good knowledge of AWS or EU cloud providers
- Good knowledge of Python
- Good knowledge of Unix and Bash
- Good knowledge of agile software development methodologies
- Good knowledge of infrastructure as code (Terraform, CloudFormation)
- Good knowledge of messaging services and platforms (Kafka, Redis, RabbitMQ)
- Knowledge of data security measures (encryption mechanisms; ML security a plus)
- Knowledge of NoSQL databases such as OpenSearch, MongoDB
- Knowledge of query languages (SQL, Hive, Pig) and information extraction
- Experience with data analytics over big datasets, non-structured databases and data lakes
- Experience with monitoring and logging tools (ELK stack, Prometheus, Grafana, OpenTelemetry, CloudWatch)
- Experience with model testing and model validation in production environments
- Ability to write clear and structured technical documentation
- EQF Level 6 or higher in Computer Science, Information Technology, or Systems Engineering
- Strong problem-solving and analytical skills
- Strong team spirit and experience working effectively within a collaborative, international/multi-cultural team
- Excellent communication skills to articulate complex technical concepts
Nice to have
- Knowledge of the European Commission grant management ecosystem (eGrants)
Full description
About Cronos Europa
Cronos Europa is a leading IT and digital transformation partner dedicated exclusively to European Institutions and agencies, delivering mission-critical solutions that shape Europe's digital future. As part of the Cronos Group, one of the most innovative and fastest-growing tech ecosystems in Europe, we benefit from a vast pool of expertise and cutting-edge capabilities. With over 1,000 specialists across Belgium, Luxembourg, and the Netherlands, we combine deep institutional knowledge with strong engineering excellence to support long-term, high-impact EU programmes requiring reliability, scalability, and innovation.
About the job
We are looking for an MLOps / LLMOps Engineer to design, deploy, monitor and optimise production-grade machine learning and large language model systems within secure hybrid on-premise and cloud environments. The role combines ML engineering, infrastructure automation, containerisation, security awareness and close collaboration with data science and backend development teams.
Responsibilities
- Design, build, and maintain production-grade ML systems with a strong focus on Large Language Models (LLMs) in a secure hybrid on-prem / cloud infrastructure.
- Deploy, test, manage and monitor ML models in different containerised environments.
- Work closely with data scientists and back-end developers to build, test, integrate and deploy ML models.
- Evolve the LLMOps lifecycle: data preparation, fine-tuning, evaluation, deployment, monitoring, and iteration. Develop evaluation frameworks for LLM quality, robustness, and regression tracking.
- Analyse performance metrics and troubleshoot issues to ensure high availability and reliability.
- Design CI/CD pipelines, use orchestration solutions and data versioning tools.
- Creating automated anomaly detection systems and constant tracking of its performance and optimising ML pipelines for scalability, efficiency and cost-effectiveness.
- Provision of security studies, security assessments or other security matters associated with information system projects.
- Provision of support and guidance to other team members on LLMOps/MLOps practices.
Specific expertise
- Excellent knowledge of managing an on-prem and/or cloud LLM / MLOps infrastructure.
- Excellent knowledge of containerisation and orchestration platforms (e.g. Kubernetes, Docker, Podman, EKS, PKS). Good knowledge of MLflow, TensorFlow (TFX) or equivalents.
- Good knowledge of Airflow.
- Good knowledge of AWS or EU cloud providers.
- Good knowledge of Python.