Senior Machine Learning Engineer

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. You will also play a crucial part in leading and contributing to the design and deployment of ML prediction endpoints. Working alongside System Engineers to establish the ML lifecycle management environment and improve coding practices is essential. We invite those motivated by innovation to join our dynamic team. Responsibilities 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 the objectives and purposes of models developed by data scientists, and build scalable data preparation pipelines Requirements 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 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