Senior Machine Learning Engineer

at  Epam Systems

We are currently searching for a Senior Machine Learning Engineer to join our remote team. The selected candidate will have a key role in contributing to the design, development, and operational lifecycle of our ML pipeline, adhering to industry best practices.
In this role, your responsibilities will include designing, creating, maintaining, troubleshooting, and optimizing ML pipeline steps. You will also contribute to the design and implementation of ML prediction endpoints. Collaborating with System Engineers to configure the ML lifecycle management environment and promoting better coding practices will be essential.
For those who are passionate about innovation, we encourage you to submit your application and become part of our dynamic team!
We accept CVs in English only.

REQUIREMENTS

• Minimum of 3 years programming language experience, ideally in Python, and strong SQL knowledge
• Robust MLOps experience (Sagemaker, Vertex, or Azure ML)
• Intermediate level in Data Science, Data Engineering, and DevOps Engineering
• Experience with at least one project delivered to production in an MLE role
• Expertise in Engineering Best Practices
• Practical experience in implementing Data Products using the Apache Spark Ecosystem (Spark SQL, MLlib/SparkML) or alternative technologies
• Experience with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.)
• Proficiency in 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 major Cloud Provider 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.)

• Contribution to the design, development, and operational lifecycle of the ML pipeline based on best practices
• Design, creation, maintenance, troubleshooting, and optimization of ML pipeline steps
• Ownership and contribution to the design and implementation of ML prediction endpoints
• Collaboration with System Engineers to configure the ML lifecycle management environment
• Writing specifications, documentation, and user guides for developed applications
• Promotion of improved coding practices and repository organization in the science work cycle
• Establishment and configuration of pipelines for projects
• Identification of technical risks and gaps, and devising mitigation strategies
• Collaboration with data scientists to productionalize predictive models, understanding the scope and purpose of the models built by data scientists, and creating scalable data preparation pipelines