Problem: The existing cloud infrastructure was using older technologies such as virtual servers to host critical software applications, which were not easily scalable, expensive, and required IT support on a regular basis (e.g., patch management).

Solution: Transitioned to a cloud-native containerized and serverless architecture, utilizing services such as Docker, Kubernetes, and serverless computing, which improved scalability, reduced costs significantly, and accelerated time-to-market for new features. The enhanced infrastructure resulted in a solution that was dynamically scalable based on user load.

Problem: Legacy software was a monolithic application, which had grown far too large for a single code base. The monolithic application was difficult and time-consuming to test, and introduced risk to customer experience each time it was updated and deployed. It had also become unstable, resulting in significant customer-facing outages.

Solution: Led a transformation and modernization strategic project to migrate the legacy application to a services-oriented design by breaking out major functionality from the monolith to separate software services. This significantly reduced the complexity and code from the legacy application, increased the capacity of the engineering team, greatly decreased Software QA and testing efforts, and ultimately resulted in measurable increases in customer satisfaction and service availability.

Problem: Due to a recent acquisition by a large Private Equity firm, the Company was required to migrate away from their use of a previous private data center, into their own public cloud infrastructure. This migration was required to be performed without losing any data, and without impact to existing customers.

Solution: Developed and executed a cloud migration strategy and detailed project plan, resulting in moving all applications and data storage to a scalable AWS cloud environment, without any loss of data, and without any noticeable customer impact.

Problem: The Company was developing a nationwide network of fully automated hot food kiosks, which required the kiosks to be reliably connected to either 4G/5G, WiFI or a hardwired Ethernet at all times. The operating software must work flawlessly in any of the network configurations, which also included transmission of secure payment information.

Solution: Researched and implemented a product solution which included an embedded 4G/5G, WiFi, Ethernet router to securely connect each of the kiosks to a central cloud-based infrastructure as well as dedicated and secure links to payment systems. The solution was easy to configure, and featured a centralized monitoring and management platform from which each of the remote units could be monitored and controlled. The solution was also certified as PCI DSS compliant, to support digital and unattended payment operations.

Problem: The Company was in need of thought-leadership in its industry, and in need of several new features which would set it apart from its competitors.

Solution: Architected, designed, and developed a unique generative AI feature, aimed at helping customers obtain product, field, and customer support information, as well as best practices for usage of the company products in an easy-to-use AI enabled chat. The AI was trained on proprietary company product and support materials, so it could provide the most accurate and company-specific guidance to customers. The feature was announced at an industry trade show, where the company was successful in demonstrating its thought leadership in its market.

Problem: The Company was currently using manual, time-consuming, and error-prone processes for building, packaging, and deploying its software and applications to its cloud-based production system infrastructure.

Solution: Utilizing best practices of Agile Methodologies, led the design and implementation of continuous integration and continuous deployment (CI/CD) techniques along with automated build and deployment pipelines, resulting in a high degree of automation for all software and development operations. The resulting automation significantly reduced engineering effort and overhead, increased speed and velocity of the engineering teams, with the ability to shift spare engineering capacity to more meaningful efforts for the Company.

Problem: The company employs a product development department of 70+ cross-functional professionals including Software Engineering, QA Engineering, Data Engineering, Business Intelligence, Product Owners, Cloud Engineers.

Solution: Led the department as VP of Engineering. Organized the department into 7 to 8 cross-functional teams, with the goals of minimizing cross-team dependencies, increasing velocity (time to market), allowing each team to function as independently as possible.

Problem: The company is an early-stage startup, and requires a product and technology roadmap and solution architecture to be defined so the engineering team can start building.

Solution: Defined the software and cloud architecture and subsequently the product and technology roadmaps for several early-stage and start-up companies.

Problem: An early-stage healthcare company is offering a transformative product and service offering to replace in-hospital image storage IT equipment with a pay-as-you-go cloud-based medical image management (SaaS) solution.

Solution: Defined the product and software architecture, developed an in-hospital image capture system for Cardiac Catheterization labs, and led the engineering teams to develop a patented and groundbreaking image capture, transmission, storage, and web-based viewing SaaS solution. The solution was ultimately acquired by a company in the Clinical Trials business, to support worldwide and secure image sharing.

Problem: The company needs to be able to dynamically adjust its web services and application compute capacity to be responsive to user demand during peak hours, as well as to scale down to conserve expenses during off-peak hours.

Solution: Led the cloud architecture as CTO across several companies, utilizing containerized technologies including Docker, Kubernetes, AWS Fargate, AWS ECS, Azure Kubernetes Service, and Azure Container Instances to create dynamically scalable systems, to meet peak demand, providing redundancy and failover capabilities, while also saving on expenses by scaling down during off-peak hours.

Problem: This healthcare company required large amounts of data inputs from a variety of sources including healthcare IoT devices, continuous glucose monitors (CGMs), data from electronic medical records, as well as bioinformatics and genomics information. Data science computations need to be continuously run on this large data set.

Solution: Led the solution architecture and engineering teams as CTO to design a big-data solution capable of supporting both real-time data inputs from sources such as CGMs, but also able to access large DNA and genomics datasets during runs of data science algorithms to support complex prediction of disease based on an individual’s personal healthcare and DNA data. The big data solution was easily accessible, which supported separate teams designing business intelligence and analytics dashboards.