Brussels, BE
Live, first seen by us 19 days agoEmployment type in the employer’s posting: Full-TimeFrom the employer’s posting
Design, build and maintain production ML systems focused on large language models
Deploy, test, manage and monitor ML models in containerised environments
Build LLM evaluation frameworks and track regressions
Design CI/CD pipelines, orchestration and data versioning
Analyse performance metrics and troubleshoot for high availability
Design, build, and maintain production-grade ML systems with a strong focus on Large Language Models (LLMs)
Deploy, test, manage and monitor ML models in different containerised environments.
Develop evaluation frameworks for LLM quality, robustness, and regression tracking.
Design CI/CD pipelines, use orchestration solutions and data versioning tools.
Analyse performance metrics and troubleshoot issues to ensure high availability and reliability.
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.
In the employer’s words · Read in context
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Day 19
Similar jobs usually come down between day 6 and day 69.
At Cronos Europa, jobs usually come down by about day 15.
From 4,397 similar postings that came down, and 12 of Cronos Europa’s. Coming down is not always a hire.
8 open jobs
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- Good knowledge of Python. - Good knowledge of Unix and Bash. - Good knowledge agile software development methodologies.
- Good knowledge of AWS or EU cloud providers. - Good knowledge of Python. - Good knowledge of Unix and Bash.
- Knowledge of NoSQL databases, such as OpenSearch, MongoDB, etc. - Knowledge of query languages, such as SQL, Hive, Pig, etc. and with information extraction. - Experience with data analytics over big datasets, non-structured databases as well as data lakes.
- Good knowledge of Airflow. - Good knowledge of AWS or EU cloud providers. - Good knowledge of Python.
- 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.
- Experience with data analytics over big datasets, non-structured databases as well as data lakes. - Experience with monitoring and logging tools (e.g. ELK stack, Prometheus, Grafana, OpenTelemetry, Cloudwatch). - Experience with model testing and model validation in production environments.
- Good knowledge of infrastructure as code (Terraform, CloudFormation). - Good knowledge of messaging services and platforms (e.g. Kafka, Redis, RabbitMQ). - Knowledge of data security measures (knowledge of encryption mechanisms and ML security is considered a plus).
- Knowledge of data security measures (knowledge of encryption mechanisms and ML security is considered a plus). - Knowledge of NoSQL databases, such as OpenSearch, MongoDB, etc. - Knowledge of query languages, such as SQL, Hive, Pig, etc. and with information extraction.
- Good knowledge agile software development methodologies. - Good knowledge of infrastructure as code (Terraform, CloudFormation). - Good knowledge of messaging services and platforms (e.g. Kafka, Redis, RabbitMQ).
- 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.
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
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If you wish to integrate a dynamic structure on a human scale while working with the latest technologies, don't wait anymore and join Cronos !
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Brussels, BE
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.
No clear work-authorization passage found. Eligibility is unconfirmed.