Hybrid Machine Learning Infrastructure Engineer

at Uni Systems

• Job title: Machine Learning Infrastructure Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, AWS, Azure, Cassandra, Cloud, Docker, ElasticSearch, Grafana, HBase, Kafka, Kubernetes, Linux, MongoDB, NoSQL, Prometheus, Python, RabbitMQ, Redis, SQL, Tensorflow, Terraform, Unix
• Location requirements: Hybrid, Brussels, Belgium

About the role

• Machine Learning Infrastructure Engineer developing and maintaining ML Ops infrastructure for AI applications. Collaborating with data scientists and developers in the dynamic technology landscape of Belgium.

Responsibilities

• Design, implement and maintain a scalable, reliable and secure hybrid cloud ML Ops infrastructure to deploy, test, manage and monitor ML models in different environments;
• Development and maintenance of software applications in the field of Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL) and/or Artificial Intelligence (AI);
• Work closely with data scientists and back-end developers to build, test, integrate and deploy ML models;
• 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.;
• Design the IT architecture for solutions in the NLP / ML / AI fields, and coordinate its implementation considering master- and meta-data management concepts;
• 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 MLOps practices.

Requirements

• Strong experience managing on-premises and/or cloud MLOps infrastructure.
• Proficient with containerization and orchestration platforms (e.g., Kubernetes, Docker, Podman, EKS, PKS).
• Experience with ML workflow tools such as MLflow, TensorFlow (TFX), or equivalents, and workflow orchestration using Airflow.
• Hands-on experience with cloud platforms (AWS and/or Azure) and infrastructure as code (Terraform, CloudFormation).
• Skilled in Python programming, Unix/Linux, and Bash scripting.
• Familiar with agile software development methodologies.
• Experience with messaging services (Kafka, Redis, RabbitMQ).
• Knowledge of data security measures, including encryption mechanisms; ML security is a plus.
• Familiarity with NoSQL databases (Elasticsearch, MongoDB, Cassandra, HBase) and query languages (SQL, Hive, Pig).
• Experience with big data analytics, unstructured databases, and data lakes.
• Proficient with monitoring and logging tools (ELK stack, Prometheus, Grafana, OpenTelemetry, CloudWatch).
• Experience with model testing and validation in production environments.
• Solid understanding of on-prem or cloud solutions for data science applications.
• Language skills: English (C1); French (C1) is an advantage.
• *Desirable certifications:*
• AWS Certified Machine Learning.
• Microsoft Azure AI Engineer Associate