[Hiring] Senior Machine Learning Engineer @EPAM Systems

Role Description

We are seeking a skilled Senior Machine Learning Engineer to join our remote team. The successful candidate will play a key role in the design, development, and management of our ML pipeline, following industry-standard methodologies.

In this position, you will focus on:

• Constructing, deploying, maintaining, diagnosing, and enhancing steps within the ML pipeline.
• Leading and contributing to the design and deployment of ML prediction endpoints.
• Collaborating with System Engineers to establish the ML lifecycle management environment and improve coding practices.

We invite those motivated by innovation to join our dynamic team.

Qualifications

• 3+ years 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.

Requirements

• 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.