Job Summary
The Machine Learning Builder will develop, deploy, and operate production-grade AI, machine learning, and analytical solutions across Claims and Payment Integrity, Customer Service, and Technology workflows within the healthcare domain. The role focuses on advanced analytics, anomaly detection, predictive modeling, and multimodal AI systems that combine structured data, natural language processing, embeddings, deep learning, and generative AI. The ideal candidate will demonstrate strong technical ownership and a production-focused mindset while delivering scalable, secure, maintainable, and explainable ML solutions that address complex business problems.
Key Responsibilities
• Design, build, deploy, and operate production AI, machine learning, and analytical systems focused on claims integrity, customer service, and technology workflows.
• Develop and integrate multimodal systems combining structured data, natural language processing (NLP), embeddings, deep learning, and generative AI to improve decision-making and output explainability.
• Engineer robust feature pipelines, population and target definitions, model evaluation frameworks, and scoring architectures.
• Implement model explainability, monitoring, and drift detection to maintain model quality and reliability.
• Collaborate with MLOps, data engineering, and platform teams to ensure CI/CD, observability, security, compliance, and auditability of deployed models and services.
• Provide operational support for ML systems, including troubleshooting, root-cause analysis, and continuous improvement in production environments.
• Apply modern software engineering practices, including source control, automated testing, infrastructure as code, containers, and deployment automation.
• Conduct data discovery, curation, and integration activities, diagnosing and resolving data pipeline issues as needed.
• Collaborate with AI/Automation and business teams to design end-to-end solutions that deliver measurable operational and business outcomes.
• Work independently in ambiguous environments, make sound technical trade-offs, and deliver high-velocity, reviewable work.
Required Qualifications
• Hands-on experience developing and deploying production-grade AI, machine learning, and analytical systems.
• Experience with advanced analytics, anomaly detection, predictive modeling, and production AI solutions.
• Experience developing multimodal AI systems using structured data, NLP, embeddings, deep learning, and generative AI.
• Knowledge of feature engineering, model evaluation, scoring architectures, model explainability, monitoring, and drift detection.
• Experience collaborating with MLOps, data engineering, and platform teams to support production model deployment.
• Understanding of CI/CD, observability, security, compliance, and auditability requirements for ML systems.
• Experience with source control, automated testing, infrastructure as code, containers, and deployment automation.
• Ability to perform data discovery, data curation, data integration, and pipeline troubleshooting.
• Strong problem-solving skills, technical ownership, and the ability to translate complex business problems into maintainable analytical solutions.
• Ability to work autonomously, make sound technical decisions, and deliver solutions in fast-paced and ambiguous environments.
