About the job
Founded in 1998, Inovalon stands at the forefront of transforming the healthcare ecosystem through the power of technology and data. We are committed to enabling our clients' success by providing innovative, data-driven solutions that enhance healthcare outcomes and economics. At Inovalon, we believe that our growth is intrinsically linked to the success of our customers, which is why we focus on empowering them with comprehensive insights and actionable solutions.
As a unified team at Inovalon, we are dedicated to addressing the most pressing challenges in healthcare. Our mission-driven culture fosters inclusion and innovation, creating value for not only our clients but also the millions of patients and members they serve.
About Us:
Inovalon is a premier healthcare technology firm that is revolutionizing the healthcare landscape with state-of-the-art AI and machine learning solutions. Our mission is to harness cutting-edge technology to improve health outcomes and streamline healthcare processes.
Role Overview
As a Machine Learning Operations (MLOps) Engineer, you will be instrumental in designing, building, and managing the infrastructure and tools that enable comprehensive ML workflows on AWS platforms, including SageMaker, Bedrock, and Snowflake Cortex. You will collaborate closely with data scientists, ML engineers, and platform teams to ensure that models are robust, secure, and scalable in production environments.
Key Responsibilities
- Design, implement, and maintain CI/CD pipelines for ML models and data workflows utilizing AWS-native services and infrastructure-as-code.
- Operationalize models developed on SageMaker, Bedrock, and Snowflake Cortex, including feature pipelines, training, batch/real-time inference, and continuous monitoring.
- Create and oversee data pipelines and feature stores using AWS Glue, Lambda, Step Functions, and Snowflake.
- Establish observability for ML systems (logging, metrics, tracing, drift/quality monitoring) and define service-level objectives (SLOs) and service-level agreements (SLAs) for production ML services.
- Automate environment provisioning, configuration, and dependency management across development, testing, and production environments.
- Collaborate with security and compliance teams to ensure ML workloads adhere to healthcare, privacy, and regulatory standards, including HIPAA.
- Work alongside ML engineers and data scientists to transition notebooks and prototypes into robust, maintainable services.
- Contribute to best practices, standards, and documentation for ML platforms and operations throughout the organization.
