About the project

• You’ll be joining a strategic data and machine learning initiative focused on delivering intelligent, scalable, and business-driven solutions. The team works on building and optimizing recommendation engines, deploying machine learning models to production, and enabling data-driven decision-making across global business units.

Your responsibilities

• Design, implement, and maintain end-to-end ML pipelines and data workflows
• Develop, test, and deploy machine learning models that support business goals
• Refactor and optimize legacy solutions to improve performance, maintainability, and scalability
• Integrate new data sources and improve existing data ingestion processes
• Collaborate with data scientists, analysts, and engineers to productionize ML prototypes
• Troubleshoot issues in data pipelines and ML infrastructure, ensuring stability and efficiency
• Follow DevOps best practices using Azure DevOps for deployment, version control, and task management
• Maintain clear documentation of solutions, workflows, and architectural decisions

Our requirements

• Solid experience (3+ years) in designing and building machine learning solutions in production
• Proficiency in Python with strong knowledge of data processing using Apache Spark
• Experience in developing robust, scalable data pipelines for structured and unstructured data
• Good understanding of the full ML lifecycle – from model development to deployment and monitoring
• Hands-on experience with containerization and orchestration tools (Docker, Kubernetes)
• Familiarity with CI/CD practices, ideally using tools like Azure DevOps
• Strong debugging, refactoring, and performance tuning skills for ML codebase

Optional

• Familiarity with LLMs, NLP or Generative AI
• Knowledge of cloud platforms (GCP, Azure), especially services like BigQuery, Dataproc, GCS
• Experience with workflow orchestration tools like Apache Airflow

Technologies we use