EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.
We are seeking an experienced Senior Machine Learning Engineer to join our team. The ideal candidate will take on leading roles in designing, developing, and optimizing our machine-learning platform. Your contributions will drive the success of our prediction models in real-world applications.

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.)

• Contribute to the design, development, and operational lifecycle of the ML pipeline based on best practices
• Design, create, maintain, troubleshoot, and optimize ML pipeline steps
• Own and contribute to the design and implementation of ML prediction endpoints
• Collaborate with System Engineers to configure the ML lifecycle management environment
• Write specifications, documentation, and user guides for developed applications
• Promote improved coding practices and repository organization in the science work cycle
• Establish and configure pipelines for projects
• Identify technical risks and gaps, and devise mitigation strategies
• Collaborate with data scientists to productionalize predictive models, understand the scope and purpose of the models built by data scientists, and create scalable data preparation pipelines