Job Description: We are seeking a Lead Machine Learning Engineer to join our remote team. As a key member of our engineering team, you will contribute to the design, development, and operation of our machine learning pipeline. Key Responsibilities: - Contribute to the ML pipeline design, development, and operation based on industry best practices - Designing, creating, maintaining, troubleshooting, and optimizing ML pipeline steps - Taking ownership and contributing to the design and implementation of ML prediction endpoints - Collaborating with System Engineers to configure the ML lifecycle management environment - Writing specifications, documentation, and user guides for developed applications - Supporting the improvement of coding practices and repository organization within the science work cycle - Establishing and configuring pipelines for various projects - Continuously identifying technical risks and gaps and formulating mitigation strategies - Collaborating with data scientists to translate predictive models into production, understanding their scope and purpose, and creating scalable data preparation pipelines Requirements: - A minimum of 5 years' experience with a programming language, preferably Python, along with a strong knowledge of SQL - A proven history of leading and mentoring an engineering team - Demonstrable MLOps experience (Sagemaker, Vertex, or Azure ML) - Intermediate proficiency in Data Science, Data Engineering, and DevOps Engineering - A record of 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 equivalent technologies - Familiarity with Big Data technologies (e.g., Hadoop, Spark, Kafka, Cassandra, GCP BigQuery, AWS Redshift, Apache Beam, etc.) - Proficiency with automated data pipeline and workflow management tools such as Airflow, Argo Workflow, etc - Experience with different data processing paradigms (batch, micro-batch, streaming) - Practical experience working with a major Cloud Provider such as AWS, GCP, and Azure - Experience 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 various types of databases (Relational, NoSQL, Graph, Document, Columnar, Time Series, etc.) - Fluency in English communication at a B2+ level Nice to Have: - Practical experience with Databricks MLOps-related tools/technologies like MLFlow, Kubeflow, TensorFlow Extended (TFX) - Experience with performance testing tools such as JMeter or LoadRunner - Knowledge of containerization technologies like Docker