Machine Learning Engineer

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 till 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 ( GCP experience preferred )
• 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
• Very 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
• Implement end-to-end solutions across the full breadth of ML model development lifecycle, and working hand in hand with the scientists from the point of data exploration for model development to the point of building features, ML pipelines and deploying them in production
• 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
• Contributing to and re-using community best practice

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