Uni Systems

Machine Learning Infrastructure Engineer

Uni Systems

full-time

Posted on:

Location Type: Hybrid

Location: Brussels • Belgium

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Tech Stack

AirflowAWSAzureCassandraCloudDockerElasticSearchGrafanaHBaseKafkaKubernetesLinuxMongoDBNoSQLPrometheusPythonRabbitMQRedisSQLTensorflowTerraformUnix

About the role

  • 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
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
Natural Language ProcessingMachine LearningDeep LearningArtificial IntelligencePythonUnix/LinuxBash scriptingCI/CD pipelinesData versioningBig data analytics
Soft Skills
collaborationtroubleshootingperformance analysisguidancecommunication
Certifications
AWS Certified Machine LearningMicrosoft Azure AI Engineer Associate