Machine Learning

Summary

Join Xebia as a Machine Learning Engineer to work with data scientists and analysts on creating and deploying new models and ML systems, implementing end-to-end solutions across the full breadth of ML model development lifecycle.

Requirements

• Ability to start immediately
• Openness to work daily between 18-19.00 pm CET
• University or advanced degree in engineering, computer science, mathematics, or a related field
• 3+ years' experience developing and deploying machine learning systems into production
• Experience working with big data tools: Spark, Hadoop, Kafka, etc
• Experience with at least one cloud provider solution (AWS, GCP, Azure) and understanding of serverless code development
• Efficiency with object-oriented/object function scripting languages (Python required)
• Efficiency with Python data-handling libraries like Pandas or Pyspark
• Efficiency in SQL for data consumption and transformation
• Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, continuous deployment, code reviews, design documentation
• Working experience with native ML orchestration systems such as Kubeflow, Vertex AI Pipelines, Airflow, TFX
• Good verbal and written communication skills in English
• Work from the European Union region and a work permit are required

Responsibilities

• Working with data scientists and analysts to create and deploy new models and ML systems
• Implementing end-to-end solutions across the full breadth of ML model development lifecycle
• Working on batch and real time models, and operational support
• Establishing scalable, efficient, automated processes for data analyses, model development, validation and implementation
• Writing efficient and scalable software to ship products in an iterative, continual-release environment
• Writing optimized data pipelines to support machine learning models
• Contributing to and promoting good software engineering practices across the team and build cloud native software for ML pipelines

Preferred Qualifications

• Experience in working with SparkSQL, BigQuery SQL dialects
• Relevant working experience with Docker and Kubernetes
• Knowledge of data pipeline and workflow management tools
• Expertise in data engineering, analysis and processing (e.g. designing and maintaining ETLs, validating data and detecting quality issues)
• Knowledge in statistics and machine learning
• Previous experience developing predictive models in a production environment, MLOps and model integration into larger scale applications