Senior Machine Learning Engineer

Summary

Join Xebia, a leading digital solutions provider, as a Senior Cloud Engineer. Design, build, and deploy at-scale infrastructure with a focus on distributed systems. Work closely with analysts/data scientists to understand impact on downstream data models.

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
• 7+ years experience developing and deploying machine learning systems into production
• Experience working with a variety of relational SQL and NoSQL databases
• Experience working with big data tools: Hadoop, Spark, Kafka, etc
• Experience with at least one cloud provider solution (AWS, GCP, Azure) and understanding of severless code development
• Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc
• Previous experience developing predictive models in a production environment, MLOps and model integration into larger scale applications
• Experience with Machine and Deep Learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow or PyTorch
• Exposition to GenAI and solid understanding of multimodal AI via HuggingFace, Llama, VertexAI, AWS Bedrock or GPT
• Knowledge of data pipeline and workflow management tools
• Expertise in standard software engineering methodology, e.g. unit testing, test automation, continuous integration, code reviews, design documentation
• Working experience with native ML orchestration systems such as Kubeflow, Step Functions, MLflow, Airflow, TFX
• Very good verbal and written communication skills in English
• Work from the European Union region and a work permit are required

Responsibilities

• Responsible for at-scale infrastructure design, build and deployment with a focus on distributed systems
• Building and maintaining architecture patterns for data processing, workflow definitions, and system to system integrations using Big Data and Cloud technologies
• Evaluating and translating technical design to workable technical solutions/code and technical specifications at par with industry standards
• Driving creation of re-usable artifacts
• Establishing scalable, efficient, automated processes for data analysis, data model development, validation, and implementation
• Working closely with analysts/data scientists to understand impact to the downstream data models
• Writing efficient and well-organized software to ship products in an iterative, continual release environment
• Contributing and promoting good software engineering practices across the team
• Communicating clearly and effectively to technical and non-technical audiences
• Defining data retention policies
• Monitoring performance and advising any necessary infrastructure changes