ML Ops/LLM Ops Engineer - EU Institution (based in Belgium)

Joining KEYES means giving purpose to your talent.

Who are we?

Are you looking for an environment where technology is not an end in itself, but a lever to build a sustainable future? At KEYES, we are not just looking for technical expertise; we are looking for people who share our values. Do you want to continue your career and grow within one of Belgium’s leading IT companies?

KEYES - International Organizations (IO) is a dynamic global organization that takes pride in being the trusted partner of EU Institutions. With strong commitment to excellence and a 30-years track record of delivering high-quality solutions, we are dedicated to supporting the growth and success of our clients. Our Mission is to help our clients keep up with the challenges of digital transformation by providing the right talent at the right time for the right job. To this end, we are constantly looking for talented professionals who are interested in working on challenging international projects and able to deliver high-quality results within multicultural environments. Our services include (but are not limited to) modernization of solutions, digital workspaces, cloud technologies and IT security. Our Headquarters are in Brussels and we have active accounts and offices across Europe (i.e. Luxembourg, Amsterdam, Athens, Stockholm, Geneva).

More information: www.keyes.eu

Is this YOU?

For one of our main clients based in Brussels - a European Institution, we are looking for a Senior ML / LLM Engineer with expertise in on-prem and cloud LLM/MLOps infrastructure to support corporate solutions in the area of research and innovation. You will be supporting the EU policies on research, science and innovation, with a view to help create growth and jobs and tackle our biggest societal challenges.

Please note that the position is based in Brussels, on hybrid mode, therefore being based in Belgium is required.

More specifically, you will be responsible for…

• 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.

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Requirements

Are you the perfect match?

• Master’s/Bachelor’s degree;

• Minimum 7 years of experience in Data, AI, LLMs;

• 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 Institutions’ grant management ecosystem (eGrants) would be an asset.