Senior Machine Learning Engineer
With a market cap of $80 billion, this industry leader sits at the heart of America’s healthcare infrastructure—operating at an extraordinary scale while innovating with the speed of a tech startup. This organization powers one of the most extensive healthcare ecosystems in the U.S., blending digital transformation, pharmacy innovation, and clinical services to drive better outcomes across the country. From chronic care management to real-time data platforms, they’re not just adapting to the future of healthcare—they’re building it. With tens of thousands of employees and touchpoints that impact millions of lives daily, this company is uniquely positioned to influence how care is accessed, personalized, and delivered. They’re investing heavily in machine learning, cloud technologies, and modern data platforms to unlock actionable insights, drive operational efficiency, and help people live healthier lives. If you’re looking to contribute to work that has real-world impact at national scale—this is the place.
JOB DESCRIPTION
We’re seeking a highly skilled and experienced ML Ops Engineer to join our client’s advanced analytics and AI/ML engineering team. This individual will play a critical role in building and optimizing machine learning pipelines, enabling scalable model deployment, and ensuring reliable data flow across platforms.
This role will collaborate closely with data scientists, machine learning engineers, and platform teams to support the full ML lifecycle — from experimentation to production.
JOB RESPONSIBILITIES
• Design, build, and manage scalable ML pipelines using Kubeflow and Kubernetes.
• Support development and deployment of machine learning models into production environments.
• Build reusable components and infrastructure to streamline model training, evaluation, and deployment.
• Integrate with data platforms such as Snowflake to ensure efficient data access and transformation.
• Collaborate with cross-functional teams to implement MLOps best practices including versioning, monitoring, and continuous integration.
• Optimize performance and scalability of ML workflows in a cloud-native environment.
EXPERIENCE
• 7+ years of experience in machine learning operations, data engineering, or related fields.
• Strong proficiency in Python for scripting and building ML pipelines.
• Deep experience with Kubeflow for orchestrating ML workflows.
• Proven expertise with Kubernetes for container orchestration in production environments.
• Hands-on experience with Snowflake for data warehousing and integrations.
• Solid understanding of MLOps lifecycle, model deployment strategies, and CI/CD pipelines.
• Experience working in large-scale enterprise environments.
With a market cap of $80 billion, this industry leader sits at the heart of America’s healthcare infrastructure—operating at an extraordinary scale while innovating with the speed of a tech startup. This organization powers one of the most extensive healthcare ecosystems in the U.S., blending digital transformation, pharmacy innovation, and clinical services to drive better outcomes across the country. From chronic care management to real-time data platforms, they’re not just adapting to the future of healthcare—they’re building it. With tens of thousands of employees and touchpoints that impact millions of lives daily, this company is uniquely positioned to influence how care is accessed, personalized, and delivered. They’re investing heavily in machine learning, cloud technologies, and modern data platforms to unlock actionable insights, drive operational efficiency, and help people live healthier lives. If you’re looking to contribute to work that has real-world impact at national scale—this is the place.
JOB DESCRIPTION
We’re seeking a highly skilled and experienced ML Ops Engineer to join our client’s advanced analytics and AI/ML engineering team. This individual will play a critical role in building and optimizing machine learning pipelines, enabling scalable model deployment, and ensuring reliable data flow across platforms.
This role will collaborate closely with data scientists, machine learning engineers, and platform teams to support the full ML lifecycle — from experimentation to production.
JOB RESPONSIBILITIES
• Design, build, and manage scalable ML pipelines using Kubeflow and Kubernetes.
• Support development and deployment of machine learning models into production environments.
• Build reusable components and infrastructure to streamline model training, evaluation, and deployment.
• Integrate with data platforms such as Snowflake to ensure efficient data access and transformation.
• Collaborate with cross-functional teams to implement MLOps best practices including versioning, monitoring, and continuous integration.
• Optimize performance and scalability of ML workflows in a cloud-native environment.
EXPERIENCE
• 7+ years of experience in machine learning operations, data engineering, or related fields.
• Strong proficiency in Python for scripting and building ML pipelines.
• Deep experience with Kubeflow for orchestrating ML workflows.
• Proven expertise with Kubernetes for container orchestration in production environments.
• Hands-on experience with Snowflake for data warehousing and integrations.
• Solid understanding of MLOps lifecycle, model deployment strategies, and CI/CD pipelines.
• Experience working in large-scale enterprise environments.
