MLOps Engineer

Added: 7/23/2021

REF: 12470

Contract: Permanent

Location: New York, United States

Our client is a healthcare platform company that leverages technology, data science and consumer-centric design to improve the healthcare experience for patients. The platform connects patients with healthcare providers in a way that addresses the important challenges of billing and payment.

We are seeking a MLOps Engineer to work with the Data Science team to enhance our data driven decision making culture underpinned by experimentation. This next stage in our evolution will focus on using ML models and our experimental data to provide optimal personalization, recommendation and automation.

Responsibilities

  • Collaborate with data engineers, developers, and technical leads to deliver ML-based systems that can be deployed both in the cloud and on edge using AWS Sagemaker and containers
  • Partner with Data Engineering to produce data feature pipelines
  • Design machine learning feature stores in collaboration with Data Science
  • Implement a robust system for measuring and optimizing the quality of deployed algorithms and models.
  • Build and maintain the CI/CD pipeline for ML; Institute a process to handle model development, with an emphasis on auditability, versioning and data security

Skills & Experience

  • 3+ years of experience training, deploying, and monitoring machine learning models with extensive knowledge of evaluation metrics and best practices
  • Understanding of data structures, data modeling and software architecture.
  • Software Engineering proficiency in at least one high-level programming language (Java, Scala, Python or equivalent) used both for ML and automation tasks
  • Experience with relational SQL and/or NoSQL databases, i.e. AWS RDS, Snowflake.
  • Experience in machine learning operations using at least one of the popular frameworks or platforms, e.g. AWS Sagemaker
  • Experience with automated data pipeline and workflow management tools, i.e. Airflow.
  • Experience with common ML frameworks such as Spark, MLlib, Tensorflow, PyTorch, XGBoost, or scikit-learn is preferred
  • Experience working with rapid product development in an agile environment is preferred
  • A passion for bringing ML to production

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