Senior Machine Learning Engineer

at  EPAM Systems Inc

Our remote team is in need of a Senior Machine Learning Engineer who will actively take part in the ML pipeline design, development, and operation based on the best practices.
In this position, your responsibilities will include designing, creating, maintaining, troubleshooting, and optimizing ML pipeline steps. You will also have ownership and involvement in the ML prediction endpoints design and implementation. Your role will be critical in collaborating with System Engineers to configure the ML lifecycle management environment and aiding in the enhancement of coding practices.
We welcome those who are passionate about innovation to apply and join our team!
We accept CVs in English only.

REQUIREMENTS

• A minimum of 3 years of programming language experience, ideally in Python, with strong SQL knowledge
• Solid MLOps experience (Sagemaker, Vertex, or Azure ML)
• Intermediate level in Data Science, Data Engineering, and DevOps Engineering
• At least one project delivered to production in an MLE role
• Expert level in Engineering Best Practices
• Practical experience in implementing Data Products using the Apache Spark Ecosystem (Spark SQL, MLlib/SparkML) or alternative technologies
• Familiarity with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.)
• Experience with automated data pipeline and workflow management tools, i.e., Airflow, Argo Workflow, etc
• Experience in different data processing paradigms (batch, micro-batch, streaming)
• Practical experience working with at least one of the major Cloud Providers such as AWS, GCP, and Azure
• Production experience in integrating ML models into complex data-driven systems
• DS experience with Tensorflow/PyTorch/XGBoost, NumPy, SciPy, Scikit-learn, Pandas, Keras, Spacy, HuggingFace, Transformers
• Experience with different types of databases (Relational, NoSQL, Graph, Document, Columnar, Time Series, etc.)

• Participation in ML pipeline design, development, and operation based on best practices
• Role in designing, creating, maintaining, troubleshooting, and optimizing ML pipeline steps
• Ownership and involvement in the ML prediction endpoints design and implementation
• Collaboration with System Engineers to configure ML lifecycle management environment
• Specification, documentation, and user guide writing for developed applications
• Support in enhancing coding practices and repository organization in the science work cycle
• Establishment and configuration of pipelines for projects
• Constant identification of technical risks and gaps, and development of mitigation strategies
• Collaboration with data scientists to operationalize predictive models, understanding the scope and purpose of models built by data scientists, and creation of scalable data preparation pipelines