Senior Machine Learning Engineer

at  Epam Systems

We are in search of a seasoned Senior Machine Learning Engineer to join our remote team. As part of our team, you’ll be required to design and establish standardized procedures for building, deploying, and overseeing Machine Learning systems rapidly and reliably. You’ll also be tasked with providing leadership in defining and implementing tools and strategies that align with internal and external stakeholders.
We accept CVs in English only.

REQUIREMENTS

• Minimum of 3 years of experience as a Machine Learning Engineer or in a similar capacity
• Strong proficiency in programming languages, particularly Python
• Solid grasp of Data Science principles and Machine Learning Engineering model creation experience
• Expertise in Engineering Best Practices
• Comprehensive understanding of Machine Learning fundamentals
• Hands-on experience implementing Data Products using technologies like the Apache Spark Ecosystem (Spark SQL, MLlib/Spark ML) or equivalent technologies
• Experience with Big Data technologies such as Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc
• Experience with automated data pipeline and workflow management tools like Airflow, Argo Workflow, etc
• Familiarity with different data processing paradigms (batch, micro-batch, streaming)
• Practical experience with one or more major Cloud Providers such as AWS, GCP, or Azure
• Experience with at least one MLOps-related platform/technology such as AWS SageMaker, Azure ML, GCP Vertex AI/AI Platform, Databricks MLOps-related tools/technologies like MLFlow, Kubeflow, TensorFlow Extended (TFX)
• Experience integrating ML models into complicated data-driven systems in a production environment
• Fluent in English with a B2+ level

• Contribute to the refinement, establishment, and operational lifecycle of ML pipelines using industry best practices
• Design, create, maintain, troubleshoot, and enhance steps in ML pipelines
• Own and contribute to the design and execution of ML prediction endpoints
• Collaborate with System Engineers to establish an ML lifecycle management environment
• Draft specifications, documentation, and user guides for developed applications
• Support the enhancement of coding practices and organization of scientific work cycle repositories
• Set up pipelines for different projects
• Regularly identify technical risks and gaps and formulate risk mitigation strategies
• Collaborate with data scientists to transition predictive models into production, creating scalable data preparation pipelines, and understanding model scopes and purposes