professional Machine Learning Infrastructure Engineer

Uni Systems
Brussels, Belgium
3 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English, French

Job location

Brussels, Belgium

Tech stack

Agile Methodologies
Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Software Applications
Azure
Bash
Big Data
Unix
Cloud Computing
Information Systems
Databases
Data Security
Query Languages
Elasticsearch
HBase
Hive
Python
Machine Learning
Meta-Data Management
MongoDB
Natural Language Processing
NoSQL
RabbitMQ
Redis
Cloud Services
TensorFlow
Prometheus
SQL Databases
Management of Software Versions
Data Logging
System Availability
Grafana
IT Architecture
Deep Learning
Cloudformation
Containerization
Data Lake
Kubernetes
Cassandra
Kafka
Machine Learning Operations
Cloudwatch
Terraform
Docker
ELK

Job description

  • 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.

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