machine learning engineer

EPAM Systems provides software engineering, digital platform engineering, and product design services to global clients. The company focuses on delivering high-quality digital solutions across various business domains, including finance, healthcare, and e-commerce, while fostering professional growth and collaboration within a diverse international community.

• Contribute to the design, development, and management of an ML pipeline following best practices;
• Develop, deploy, maintain, troubleshoot, and enhance ML pipeline stages;
• Lead the design and deployment of ML prediction endpoints;
• Collaborate with System Engineers to establish the ML lifecycle management setup;
• Author specifications, documentation, and user guides for applications;
• Enhance coding practices and organize repositories within the scientific workflow;
• Configure pipelines for various projects;
• Detect technical risks and discrepancies and formulate mitigation plans;
• Partner with data scientists to operationalize predictive models, understand model objectives, and build scalable data preparation pipelines.

• 3+ Years of programming experience, ideally in Python, with robust SQL knowledge;
• Profound MLOps experience (e.g., Sagemaker, Vertex, Azure ML);
• Intermediate proficiency in Data Science, Data Engineering, and DevOps Engineering;
• At least one project delivered to production in an MLE role;
• Expertise in Engineering Best Practices;
• Practical experience implementing Data Products using 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);
• Experience with automated data pipeline and workflow management tools such as Airflow, Argo Workflow;
• Experience in different data processing paradigms such as batch, micro-batch, streaming;
• Practical experience with at least one major Cloud Provider including AWS, GCP, Azure;
• Production experience integrating ML models into complex data-driven systems;
• Knowledge of DS using Tensorflow, PyTorch, XGBoost, NumPy, SciPy, Scikit-learn, Pandas, Keras, Spacy, HuggingFace, Transformers;
• Experience with various types of databases including Relational, NoSQL, Graph, Document, Columnar, Time Series;
• Nice to have: Practical experience with Databricks MLOps-related tools or technologies such as MLFlow, Kubeflow, TensorFlow Extended (TFX), experience with performance testing tools like JMeter or LoadRunner, familiarity with containerization technologies like Docker.

• Flexible schedule with remote work or office/coworking options in Ukraine;
• Necessary equipment provided;
• Opportunity to change projects and technology stacks;
• Access to professional growth programs, including internal training, certification in AWS, GCP, or Azure, and corporate learning platforms;
• Vacation and sick leave;
• Voluntary Medical Insurance programs with options for family members;
• Support during significant life events and psychological comfort services;
• Relocation opportunities may be available for eligible candidates.