Machine Learning Engineer (GCP)

For more than 20 years, our global network of passionate technologists and pioneering craftspeople has delivered cutting-edge technology and game-changing consulting to companies on the brink of AI-driven digital transformation. Since 2001, we have grown into a full-service digital consulting company with 5500+ professionals working on a worldwide ambition. Driven by the desire to make a difference, we keep innovating. Fueling the growth of our company with our knowledge worker culture. When teaming up with Xebia, expect in-depth expertise based on an authentic, value-led, and high-quality way of working that inspires all we do.

About the Role

We are looking for a Machine Learning Engineer (MLE) to join our team. In this role, you will work closely with data scientists and analysts to develop and deploy new machine learning models and systems. The MLE will be involved in the full breadth of the ML lifecycle, from data exploration, feature engineering, and pipeline building to deployment in production environments. You will work on both batch and real-time models and play a key role in ensuring scalability and efficiency in production environments.

What You’ll Do

Machine Learning Development

• Collaborate with data scientists and analysts to create, test, and deploy machine learning models
• Implement end-to-end solutions for the ML lifecycle, including feature engineering, model development, and production deployment
• Build both batch and real-time ML models and provide ongoing operational support.

Engineering and Scalability

• Establish scalable, efficient, automated processes for data analysis, model development, validation, and implementation
• Write optimized data pipelines that support machine learning models in production
• Contribute to cloud-native software development for ML pipelines, promoting best practices and efficient software engineering techniques.

Code Quality and Best Practices

• Write efficient, maintainable, and scalable software in an iterative, continual-release environment
• Contribute to software engineering standards such as unit testing, test automation, continuous integration, and code reviews
• Promote and re-use community best practices in software development.

Continuous Improvement and Innovation

• Stay up-to-date with the latest trends and developments in machine learning and cloud technologies, implementing new tools and approaches where relevant.
• Contribute to team’s goal of becoming a data-driven enterprise by working on key data projects, including:

1. Forecasting models for planning and allocation
2. Promotion recommendation tools
3. Pricing elasticity modeling.

• Support technical evaluations of other consultants when required, contributing to the assessment of skills and alignment with project needs

What You Bring

• Vertex AI experience is required.
• University or advanced degree in engineering, computer science, mathematics, or related field
• 3+ years of experience (mid-level), 5+ years (senior-level) developing and deploying ML systems into production
• Experience with big data tools (e.g., Spark, Hadoop, Kafka)
• Experience working with Google Cloud Platform (GCP)
• Proficiency in object-oriented and functional programming, with Python required
• Experience with Python data libraries like Pandas and PySpark
• Proficiency in SQL for data consumption and transformation (e.g., SparkSQL, BigQuery SQL)
• Expertise in data pipeline and workflow management tools
• Experience with designing and maintaining ETLs, validating data, and ensuring data quality.
• Knowledge in statistics and machine learning
• Experience developing predictive models in production and integrating ML models into larger-scale applications.