Senior Machine Learning Engineer

Inovalon was founded in 1998 on the belief that technology, and data specifically, would empower the transformation of the entire healthcare ecosystem for the better, improving both outcomes and economics. At Inovalon, we believe that when our customers are successful in their missions, healthcare improves. Therefore, we focus on empowering them with data‑driven solutions. And the momentum is building.

Together, as ONE Inovalon, we are a united force delivering solutions that address healthcare's greatest needs. Through our mission‑based culture of inclusion and innovation, our organization brings value not just to our customers, but to the millions of patients and members they serve.

Inovalonis a leading cloud‑based healthcare technology company thatleveragesdata analytics and AI to drive meaningful improvements across the healthcare ecosystem. The Senior Full‑Stack Machine Learning Engineer sits within the Insights Business Unit, which serves as Inovalon'scentral AI and machine learning hub. This team partners with Provider, Payer, and Pharmacy business units to identify, build, and deploy AI solutions that improve clinical and operational outcomes at scale.

In this role, you will contribute to both classical machine learning and generative AI applications, including LLM‑based and agentic solutions. You will work across the full model development lifecycle on a modern, cloud‑native AWS stack, collaborating closely with AI Product Managers and a distributed team of senior engineers across the U.S. and India.

• Design, train, and deploy machine learning models spanning classical ML (classification, regression, clustering, time‑series) and generative AI use cases including LLM‑based and agentic applications.
• Build and maintain cloud‑native solutions on AWS using containerized architectures (Docker, Kubernetes) to support scalable model serving and data pipelines.
• Own and contribute to the full Model Development Lifecycle (MDLC), including dataset versioning, model versioning, model registry management, and model evaluation frameworks.
• Develop and integrate Python‑based ML components that work seamlessly with existing product platforms across multiple business units.
• Collaborate with AI Product Managers across the Insights BU and partner business units (Provider, Payer, Pharmacy) to translate business needs into AI solutions.
• Apply neural networks and deep learning techniques using PyTorch for appropriate use cases alongside scikit‑learn‑based classical approaches.
• Write robust, production‑ready code following engineering best practices; participate in code and design reviews.
• Leverage AI coding tools (such as Claude Code or equivalent) as part of your daily development workflow to improve velocity and code quality.
• Mentor junior engineers and contribute to team knowledge‑sharing around ML best practices, tooling, and architecture decisions.
• Support integration of frontend components into ML‑powered features where applicable.
• Contribute to retrospectives and team process improvements; actively participate in sprint planning and end‑of‑iteration demos.
• Adhere to all HIPAA, data governance, confidentiality, and regulatory requirements in all aspects of work.
• Maintain compliance with Inovalon's policies, procedures, and mission statement, fulfilling responsibilities that support operational and financial success.

Required

• Minimum 5 years of software development experience with a strong foundation in machine learning fundamentals and model training.
• Expert‑level Python proficiency;
  Python is the team's primary language and is the highest‑priority technical requirement.
• Hands‑on experience building and deploying classical ML models in production using scikit‑learn.
• Demonstrated experience with generative AI, LLMs, or agentic application development.
• Proficiency with PyTorch and neural network architectures.
• Practical knowledge of the Model Development Lifecycle (MDLC): dataset versioning, model versioning, model registry, and model evaluation.
• AWS cloud experience, including deploying and managing cloud‑native workloads.
• Containerization experience with Docker and/or Kubernetes.
• Strong…