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.
Good knowledge of Unix and Bash.
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).
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.
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.
Ability to write clear and structured technical documentation.
Good knowledge of European Commission grant management ecosystem (eGrants) would be an asset.
EQF Level 6 or higher in a relevant field such as Computer Science, Information Technology, or Systems Engineering.
Skills and competencies
Strong problem-solving and analytical skills.
Strong organisational and time-management skills.
Strong team spirit and experience working effectively within a collaborative team.
Capability of integration in an international/multi-cultural environment.
Excellent communication skills to articulate complex technical concepts.
Attention to detail in infrastructure design and well-structured documentation.
Ability to write clean, efficient, and maintainable code.
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 !
Summary
Design, deploy, monitor, and optimize production-grade ML and LLM systems in secure hybrid on-premises/cloud environments, including CI/CD pipelines, containerized deployments, and evaluation frameworks. Requires EQF Level 6+ in computer science, IT, or systems engineering, plus Python, Kubernetes or Docker, Airflow, AWS or EU cloud providers, and strong communication.
Responsibilities
Design, build and maintain production-grade ML/LLM systems in secure hybrid on-prem/cloud infrastructure; deploy, test, manage and monitor models in containerised environments; collaborate with data scientists and backend developers; evolve the LLMOps lifecycle and evaluation frameworks; analyse performance metrics and troubleshoot issues; design CI/CD pipelines and use orchestration and data versioning tools; build anomaly detection and optimise ML pipelines; provide security assessments; support and guide team members.
Qualifications
Excellent knowledge of on-prem/cloud LLM/MLOps infrastructure, containerisation, orchestration, Python, Unix, Bash, agile development, infrastructure as code, messaging platforms, NoSQL databases, query languages, monitoring/logging, model testing/validation, and technical documentation; good knowledge of Airflow, AWS or EU cloud providers, MLflow/TensorFlow or equivalents; security and data analytics experience; EQF Level 6 or higher in a relevant field.
Education requirements
EQF Level 6 or higher in a relevant field such as Computer Science, Information Technology, or Systems Engineering.
Experience requirements
Experience with data analytics over big datasets, non-structured databases and data lakes; monitoring and logging tools; model testing and model validation in production environments.