ML Engineer

Responsibilities:

• Design, implement, and maintain MLOps architecture to support machine learning and data processing workflows
• Build and optimize complex ETL/ELT pipelines for both batch and streaming data processing (S3, Spark, Kafka, Flink)
• Deploy and manage cloud infrastructure on AWS, Azure, or GCP, including GKE, Dataproc, Dataflow, BQ, and GCS
• Implement CI/CD pipelines for machine learning and data processing solutions
• Optimize public cloud analytics and ensure scalability and performance of ML models and data pipelines
• Manage monitoring, observability, logging, and alerting systems to track performance and failures
• Work with RDBMS, NoSQL, and time-series databases, ensuring efficient data storage and retrieval
• Ensure security, compliance, and data governance in ML and data engineering workflows
• Collaborate with Data Scientists and Data Engineers to integrate ML models with production data systems

Requirements:

• 4+ years of experience in MLOps/Data Engineering
• 4+ years of experience developing and deploying machine learning systems into production
• 8+ years of experience as a data engineer or software developer, strong programming skills like Python
• experience with Spark Cluster, Airflow, Azure DevOps, CI/CD pipelines, and containerization
• experience in Spark3+, MLFlow, containerization and Python, Scikit-learn, Pandas, NumPy
• hands-on experience with Affinity with Advanced Analytics, Data Science, NLP
• deep understanding of batch and streaming data (S3, Spark, Kafka, Flink), experience in building complex ETL
• expertise in SQL and noSQL
• good understanding of RDBMS, non-SQL and time-series databases
• experience with Google Cloud Platform (GCP) and managed GCP services (GKE, GCS, BQ, Dataproc, Dataflow)
• deep knowledge of public Cloud Analytics
• knowledge of monitoring, observability, logging, alerting
• English level: B2, C1
• experience with Go, Java or Scala
• expertise in managed Azure or AWS service
• experience in Kubernetes

Offer:

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