Innovator and Technologist
With over 20 years of experience in software development and leadership, I have had the privilege of working across a diverse range of industries, from financial services to cutting-edge technology, driving impactful solutions with a focus on microservices, AI, DevOps, and chaos engineering. Currently, I serve as a Director at Citi, where I lead the engineering and validation of AI-powered cloud-native platforms for low-latency financial applications, leveraging Kubernetes/OpenShift and a variety of modern technologies. My expertise spans across DevOps, automation, environment management, and containerization, with a particular focus on optimizing production environments for resilience and efficiency. I spearhead the design of Zero Touch Deployments, Auto Checkouts, and advanced monitoring solutions, which significantly enhance delivery speed and reduce manual interventions. Previously, I held the position of Senior Vice President at Citi, where I led a globally distributed team in developing innovative solutions in the areas of chaos engineering, containerization, cloud migration, and DevOps automation. My team and I were responsible for ensuring the resilience of financial systems, optimizing performance, and integrating the latest advancements in machine learning and AI to transform operations. I have extensive experience in managing large teams, driving digital transformation, and mentoring the next generation of technology leaders. I am passionate about building robust, scalable systems and continuously learning and experimenting with new technologies to solve complex challenges. In my career, I’ve contributed to a wide array of initiatives, including developing stock record systems, creating machine learning prototypes for predictive analytics, and leading the development of online banking applications for CIBC. My deep technical knowledge is complemented by my ability to innovate, manage cross-functional teams, and lead with a focus on delivering measurable business value. When I’m not immersed in technology, I enjoy working on open-source projects, mentoring aspiring professionals, and exploring the latest trends in AI and machine learning.
Patent Number: US-20250053501-A1
Date of Patent: February 13, 2025
Abstract: This system utilizes machine learning to predict cascading failures in cloud-based applications, improving reliability and performance. The system includes an application model representing the dependencies between microservices and a machine learning algorithm that analyzes historical data to predict failure scenarios and timing for microservices. It identifies failure points and provides actionable recommendations for code revisions to mitigate failure risks.
Patent Number: 20250053499
Date of Patent: February 13, 2025
Abstract: This system generates an application model representing dependencies among microservices and uses machine learning to identify failure points. It creates chaos testing scenarios targeting identified failure points and applies them to evaluate the application under disturbance. The system then generates code revision recommendations based on chaos testing outcomes, optimizing application resilience.
Patent Number: US-12198030-B1
Date of Patent: January 14, 2025
Abstract: This technology empowers real-time adaptation for detecting and automatically correcting issues such as bias, harmful content, and intellectual property violations in outputs generated by AI models. It uses dynamic regulation alignment to ensure compliance with global governance frameworks, improving fairness, inclusivity, and scalability in machine learning models.
Patent Number: 11,847,046
Date of Patent: December 19, 2023
Abstract: A network system that provides testing, resiliency, chaos, and performance testing in a single tool that operates on cloud-based applications in real time. The system combines various validations into a single package, offering modules for functional validations, performance testing, and mechanisms for resiliency and chaos testing.
Patent Number: 11,924,027
Date of Patent: March 5, 2024
Abstract: Systems and methods for reducing wasted computational resources by detecting network operation validation anomalies in conglomerate-application-based ecosystems. The system provides a first network operation to a software application, receives a message associated with the processing, performs a validation process, and prevents invalid operations from propagating.
Patent Number: 12,111,754
Date of Patent: October 8, 2024
Abstract: Technology that evaluates the compliance of an AI application with predefined guidelines. It constructs test cases with prompts, expected outcomes, and explanations, supplies the prompts to the AI application, receives outcomes and explanations, compares them with expected results, and generates a compliance indicator.
Patent Number: 12,184,480
Date of Patent: December 31, 2024
Abstract: Systems and methods for improving the efficiency and accuracy of network operation validation anomaly detection in conglomerate-application-based ecosystems. The platform provides a network operation to a software application, generates a communication map, determines a validation status, and executes corrective actions to address anomalies.