Doran Jones Inc.

• Design and build scalable data pipelines that support machine learning and AI-driven applications • Develop and maintain ML-ready data infrastructure across structured and unstructured healthcare datasets. • Implement data ingestion, transformation, and feature engineering pipelines. • Build services that expose ML models and AI capabilities to internal applications and APIs • Work with engineering teams to integrate AI functionality into modernized healthcare platforms. • Help migrate legacy data architectures to cloud-native data platforms • Implement best practices for data governance, data quality, and observability • Collaborate with product and engineering teams to deliver AI-powered healthcare solutions • Contribute to the development of ML pipelines, model deployment workflows, and AI platform tooling

• Design and build AI-enabled applications that enhance healthcare workflows and operational systems • Develop backend services using Java and Spring Boot within a microservices architecture. • Build modern React-based user interfaces that surface AI insights and automation. • Integrate machine learning models, LLMs, and AI services into application workflows. • Develop and maintain APIs and microservices supporting AI-driven features • Collaborate with data engineers and ML engineers to integrate models into production systems. • Participate in the modernization of legacy applications into scalable cloud-native services. • Work across the stack to improve performance, scalability, and reliability • Contribute to architectural decisions and engineering best practices.

• Assess legacy data and reporting workloads to identify where automation replaces manual effort. • Lead the migration of Informatica-based data workloads to Databricks, ensuring performance, reliability, and data integrity. • Design and execute an automation-first modernization strategy for data pipelines, reporting systems, and analytics platforms. • Apply tool-assisted and AI-assisted techniques to accelerate modernization while maintaining compliance and control. • Build repeatable frameworks and patterns for: Data ingestion, transformation, and orchestration; Reporting and analytics modernization; Data validation, reconciliation, and quality controls. • Establish governance to ensure modernization efforts are consistent, auditable, and scalable. • Partner with distributed data teams to deploy and scale modernization patterns across environments. • Provide hands-on technical leadership while remaining engaged in execution.