Gaj Subudhi

Lead forward-deployed engineering for enterprise AI deployment, including use-case prioritization, platform architecture, and production implementation. Develop enterprise strategies and technical roadmaps for application modernization and data pipeline optimization.

Architected an AI-enabled crew operations platform supporting thousands of daily scheduling decisions, reducing manual intervention by approximately 35%. Delivered a mission-critical CrewControl Operations capability in under 10 weeks, eliminating a six-figure annual vendor dependency.

Designed a Generative AI transformation and vendor-transition strategy supporting more than 60 product lines, reducing migration risk and lowering schedule and compliance rework by approximately 20%. Delivered an executive-level AI transformation roadmap in three weeks, accelerating leadership decisions and funding approvals.

Reduced SDLC and operational costs by more than 20% by standardizing Generative AI tooling and automating engineering workflows. Delivered the organization's first production-grade Generative AI solution within an enterprise governance framework.

Led modernization of legacy healthcare platforms into Azure-based microservices using Spring Boot and Azure Kubernetes Service, improving scalability, resiliency, and deployment agility. Built IoT ingestion pipelines supporting near-real-time cardiac telemetry analytics and high-volume processing of connected medical-device data.

Generated nine patent-pending AI and analytics innovations that contributed to approximately 30% improvement in operational efficiency across digital claims-processing workflows. Designed a machine learning architecture for complex claims analysis, improving predictive performance and supporting more accurate and consistent claims decisions.

Won an internal engineering hackathon by designing an innovative solution to Bigtable's 10,000-column limitation, improving analytics-query performance by approximately 90%. Consolidated fragmented reporting systems into a centralized business intelligence and data-governance platform, reducing reporting costs by more than 30%.

Built a Cloudera-based enterprise data mart processing more than 10TB of engineering and simulation data daily from supercomputing environments supporting computational fluid dynamics research. Improved engineering productivity by approximately 25% by streamlining analytics workflows and increasing access to high-volume engineering and simulation data.

Delivered enterprise operational analytics and omnichannel integration capabilities supporting digital and paper-based transaction operations across more than 200 countries. Developed an enterprise tag-management framework that improved analytics governance, compliance visibility, data consistency, and operational reporting.

Master of Computer Applications degree from Utkal University, India.

Bachelor of Science with Honors in Physics from Berhampur University, India.

Postgraduate Diploma in Computer Applications from Berhampur University, India.