MLOps LLMOps Engineer

• 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.
• Analyze performance metrics and troubleshoot issues to ensure high availability and reliability.
• Design CI/CD pipelines, use orchestration solutions and data versioning tools.
• Create automated anomaly detection systems and constant tracking of their performance and optimizing 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.

What they ask

• Excellent knowledge of managing an on-prem and/or cloud LLM / MLOps infrastructure.
• Excellent knowledge of containerization 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.

Preferences.

• Good knowledge of a European organization's grant management ecosystem (eGrants) would be an asset.

Education.

• EQF Level 6 or higher in a relevant field such as Computer Science, Information Technology, or Systems Engineering.

Languages.

• Excellent knowledge of English - Level C1 in Understanding, Speaking and Writing.

Soft skills.

• Strong problem-solving and analytical skills.
• Strong organizational 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.

• Opportunity to work in an international and multi-cultural environment.
• Opportunity to work for interesting European projects managed by a European organization.