MLOps Engineer

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Are you looking for an opportunity to make a difference and help build a system that will have a positive impact on public health? What if you could find a position that is tailor‑made for your mix of development, engineering, and analytics skills? Efficient development teams make the most of their time by limiting the activities that take developers and data scientists away from writing their code.

That’s why we need you, an experienced machine learning engineer, to help us build and configure an MLOps platform in the Cloud that shortens the time it takes to get new capabilities from development to production to support mission‑critical operations.

As an MLOps Engineer on our team, you’ll use your development experience to streamline our development lifecycle from development to production. You’ll be working with a collaborative Agile development team to build and maintain Cloud software and infrastructure that supports machine learning across the enterprise. You’ll implement continuous integration and deployment to development, testing, and production environments. This is an opportunity to broaden your skill set into areas like Agile development, Cloud‑based development, containerization, and serverless while developing software that will improve public health.

As a machine learning engineer, you’ll identify new opportunities to build solutions and architecture to help your customers meet their toughest challenges.

Work with us to solve real‑world challenges and define an ML strategy for public health and protect America from health, safety, and security threats.

• Build, configure, and maintain a robust MLOps platform in the Cloud to streamline the development lifecycle from development to production, ensuring efficient deployment of machine learning models.
• Design and implement continuous integration and continuous deployment (CI/CD) workflows to automate the testing, integration, and deployment of ML models in development, testing, and production environments.
• Work closely with an Agile development team, leveraging collaborative approaches to develop, deploy, and maintain cloud‑based software and infrastructure supporting enterprise‑wide machine learning initiatives.
• Enhance and manage ML life cycles, including data management, model training, deployment, and monitoring, to ensure seamless integration and operation within production environments.
• Develop containerized applications, focusing on API design and authentication, to ensure scalable and secure deployment of ML models across cloud environments.
• Utilize distributed and cloud technologies such as Azure and Databricks to efficiently manage data and machine learning workflows, optimizing performance and scalability.
• Identify new opportunities to design and implement end‑to‑end automated data and ML pipelines, leveraging cloud services, containerization, and serverless architectures to meet the client’s toughest challenges.
• Continuously evaluate and integrate new tools and technologies such as Kubernetes, version control systems like Git, and other cloud services such as Azure Data Lake Services or Data Factory to enhance the MLOps ecosystem and improve development workflows.

• 4+ years of experience with Object‑Oriented Programming (OOP), including in Python or Py Spark
• 3+ years of experience developing software using distributed and cloud technologies, including Azure and Databricks
• 3+ years of experience leveraging MLOps platforms and Machine Learning (ML) CI/CD workflows to manage datasets and model training, deployment, and monitoring
• Experience developing containerized applications, including API design and authentication
• Knowledge of the ML lifecycle and concepts to develop an MLOps ecosystem
• Public Trust
• Bachelor’s degree