MLOps with Data Science background

Responsibilities:

• Design, implement, and maintain MLOps architecture to support machine learning models in cloud and on-premises environments
• Manage the entire ML lifecycle, including model training, monitoring, versioning, and deployment
• Optimize ETL/ELT pipelines using Apache Airflow and MLflow
• Implement monitoring, observability, logging, and alerting systems to track model performance
• Collaborate with Data Science and Data Engineering teams to ensure scalability and reliability of ML solutions
• Automate ML pipelines and deploy CI/CD practices for machine learning models
• Deploy and manage cloud infrastructure on GCP, AWS, or Azure, including managed services like GKE, Dataproc, Dataflow, BQ, and GCS
• Optimize cloud resources and implement best practices in Kubernetes

Requirements:

• 4+ years of experience in MLOps/Data Engineering
• Expertise in public cloud platforms (AWS, Azure, GCP) and their managed services (GKE, Dataproc, Dataflow, GCS, BQ)
• Hands-on experience with Kubernetes and Docker
• Strong knowledge of MLflow, Apache Airflow, TensorFlow, and sklearn
• Proficiency in Python and Go (Java or Scala is a plus)
• Experience in CI/CD for ML systems and model versioning
• Strong background in monitoring and optimizing ML models
• In-depth understanding of MLOps principles, feature engineering, and automation
• Fluent in English (B2/C1)
• Strong Data Science background, with experience in ML model training, evaluation, and optimization

Offer:

• Private medical care
• Co-financing for the sports card
• Training & learning opportunities
• Constant support of dedicated consultant
• Employee referral program
• Flexible working hours