Lead Machine Learning Engineer

at  Epam Systems

We are seeking a Lead Machine Learning Engineer to join our remote team and assist in the design, development, and operation of ML pipelines based on best practices.
In this role, you will be responsible for designing, creating, maintaining, troubleshooting, and optimizing steps in ML pipelines. You will also lead and contribute to the design and implementation of ML prediction endpoints. Your role will also involve collaborating with System Engineers to configure environments for ML lifecycle management and improve coding practices.
If you are passionate about innovation, we invite you to apply and join our team!
We accept CVs in English only.

REQUIREMENTS

• 5+ years of Python programming experience with strong SQL knowledge
• 1+ years of relevant leadership experience
• Strong MLOps experience (e.g., Sagemaker, Vertex, Azure ML)
• Intermediate level in Data Science, Data Engineering, and DevOps Engineering
• Track record of delivering at least one project to production in an MLE role
• Background in the Apache Spark Ecosystem (e.g., Spark SQL, MLlib/SparkML)
• Proficiency in automated data pipeline and workflow management tools (e.g., Airflow)
• Experience in different data processing paradigms (batch, micro-batch, streaming)
• Practical experience with at least one major Cloud Provider (AWS, GCP, Azure)
• Production experience in integrating ML models into complex data-driven systems

• Contribution to ML pipeline design, development, and operating lifecycle
• Design, creation, maintenance, troubleshooting, and optimization of ML pipeline steps
• Ownership and contribution to ML prediction endpoints design and implementation
• Collaboration with System Engineers to configure ML lifecycle management environment
• Writing specifications, documentation, and user guides for developed applications
• Support in improving coding practices and repository organization
• Establishing and configuring pipelines for projects
• Continuous identification of technical risks and gaps with strategies for mitigation
• Collaboration with data scientists to productionalize predictive models and create scalable data preparation pipelines