· Maternal health data was fragmented across devices and vendors, limiting visibility into daily pregnancy vitals. Built an AI-powered monitoring platform with React and Next.js that unified blood pressure, weight, and fetal heart rate tracking across 50+ integrations, enabling continuous monitoring throughout pregnancy.

· Vital sign alerts were delayed and unreliable under high-frequency data loads. Built real-time dashboards with Node.js and FastAPI that processed streaming vitals securely and reduced alert latency by 42%.

· High-risk pregnancy conditions were often detected late using traditional review workflows. Integrated Python-based ML models into clinical APIs to flag preeclampsia in 6% of patients and mental health risks in 30% of monitored populations earlier.

· Patients struggled to find and access pregnancy wellness services due to manual eligibility checks. Built a secure marketplace platform that automated eligibility verification and connected patients to doulas, lactation, and mental health services.

· Access control across clinics was inconsistent and difficult to manage at scale. Implemented Firebase Auth with Auth0 SSO and custom authorization to support secure multi-tenant access for OB-GYN clinics and care navigators.

· Enhanced identity management and authentication security by implementing AWS Cognito with OIDC and RBAC, supporting HIPAA-compliant user provisioning and secure multi-tenant access across enterprise healthcare partners.

· Remote monitoring devices sent data inconsistently and failed silently during outages. Designed an event-driven system with AWS Lambda and SQS that synchronized device data in real time with built-in fault tolerance.

· Orchestrated complex maternal care workflows using AWS Step Functions to coordinate Lambda services, device ingestion pipelines, and clinical alert routing with built-in retry and failure handling mechanisms.

· Cancer screening workflows required reliable delivery of complex genomic results at scale. Built the Galleri platform with React and Django, delivering multi-cancer detection results to 10K+ patients quarterly with 99.6% specificity.

· Genomic sequencing data processing was slow and costly at high volumes. Built Spark-based Python pipelines processing 5TB+ daily, enabling faster signal detection for ML models.

· Diagnostic inference pipelines introduced latency that slowed clinical workflows. Engineered a Node.js inference service with TensorFlow Serving that reduced signal processing latency by 42%.

· Sample tracking and lab workflows lacked end-to-end traceability. Built an Angular-based LIMS with PostgreSQL partitioning that managed 200K+ samples quarterly with automated chain-of-custody tracking.

· Clinical data access required strict regulatory and security controls. Designed FastAPI services with OAuth2 RBAC that met CLIA compliance and protected PHI for research partners.

· Strengthened secure authentication and user federation by integrating AWS Cognito with clinical research portals, enabling secure identity management for internal teams and external oncology partners.

· Implemented AWS Step Functions to orchestrate genomic data processing workflows, coordinating inference services, data validation, and audit logging across distributed microservices.

· Collaborated directly with oncology research institutions and enterprise healthcare partners to gather integration requirements and deliver standardized diagnostic APIs.

· Led cross-functional technical workshops to translate complex research workflows into scalable cloud-native architectures deployed on Kubernetes and AWS infrastructure.

· Pipeline health and accuracy issues were hard to detect in real time. Built Vue dashboards with Prometheus metrics to monitor genomic workflows and model performance.

· Prescription management systems struggled under high user traffic and slow response times. Built Angular and Node.js platforms with Redis caching that supported 8M+ users across 9,000+ locations.

· Insurance verification caused delays in prescription fulfillment. Built Node.js microservices that reduced Aetna claim processing time by 38%.

· Eligibility verification systems could not handle growing daily volume. Delivered APIs processing 500K+ daily requests with reliable synchronization to pharmacy systems.

· Mobile refill experiences were limited and underused. Built a React Native app with Firebase messaging that increased digital engagement by 47%.

· Inventory synchronization between pharmacies and clinical systems was manual and delayed. Designed event-driven AWS Lambda workflows to keep retail, distribution, and EHR systems in sync.

· Copayment processing required strict payment security guarantees. Integrated Stripe with PCI-DSS compliant tokenization for secure prescription payments.

· Pharmacists relied on manual medication review processes. Built Django-based automation using FHIR HL7 to streamline drug interaction checks.

· Deployments risked outages across pharmacy systems. Implemented ECS deployments with Jenkins and blue-green releases, enabling safe bi-weekly releases.

· Healthcare clients relied on manual workflows and disconnected systems. Built React and Node.js applications that automated hospital operations and documentation.

· Operational metrics were unavailable in real time. Delivered Power BI dashboards backed by Azure SQL and APIs to provide live insights.

· Clinical documentation systems lacked customization and automation. Built SharePoint solutions with React components and automated workflows.

· Administrative teams spent excessive time on repetitive tasks. Implemented Power Automate and Azure Functions workflows that reduced manual entry by 65%.

· EHR integrations were inconsistent across practices. Designed Node.js APIs with Prisma and Azure AD authentication to unify access.

· Appointment scheduling systems were inefficient and error-prone. Built Angular scheduling platforms that optimized booking for 12 healthcare clients.

· Legacy system migrations risked downtime during hospital mergers. Built Python and SSIS pipelines that consolidated systems with minimal disruption.

· Deployment errors slowed client delivery timelines. Implemented Azure DevOps CI/CD with automated testing, reducing deployment errors by 40%.