Shelly Saunders, PhD

With over 35 years of experience in software engineering, I bring expert knowledge in managing software development teams, designing technical architectures, and development with Microsoft technologies, along with a broad expertise in a variety of other technologies.

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SOFTWARE ENGINEERING MANAGER and CTO

2015 – Present (Remote) Desktop Alert Inc, United States

– Spearheaded the design and development of a microservice based SaaS platform providing an emergency alert system to UK schools.
– Led the roll-out of the on-premises platform to NATO sites worldwide.
– Developed code with .NET, SQL Server, Angular, NoSQL, Kafka, Redis, Azure Functions. Blazor, MAUI, WinForms, Xamarin Mac, iOS & Android
– Achieved accreditation from DISA, FEMA and NATO.

AZURE INTEGRATION ARCHITECT

2020 – 2021 (Remote) Safe Online, Denmark

– Part-time consultant designing the cloud architecture of a start-up privacy management system for companies seeking to process sensitive data in compliance with GDPR. Contributed code in .NET, SQL Server, MongoDB, Azure Service Bus, Azure Functions & Event Grid

TECHNICAL ARCHITECT

2010-2013 (Hybrid) London Metal Exchange, United Kingdom

– Designed and developed an application in ASP.NET, SQL Server and AngularJS for LME staff to provide real-time price calculations to traders.

TECHNICAL ARCHITECTURE TEAM LEADER

2005-2008 (Onsite) ACE Global Reinsurance, United Kingdom

– Provided technical leadership to a team of 30+ developers working on a service-oriented integration of key legacy systems within the company. Developers worked with diverse technologies such as WebSphere, J2EE, ASP.NET, Oracle, SQL Server, MQ Series.

CHIEF TECHNICAL ARCHITECT

1999-2004 (Onsite) Mourant Financial Services Ltd, United Kingdom

– Led the design and development of a multi-tenanted B2C web application used by blue-chip companies worldwide. 30+ developers worked onsite and in India using .NET, SQL Server, MSMQ.

PhD PROJECT Machine Learning Optimization of Service Oriented Architectures

2012 Software Quality of Service of Service-Oriented Architectures
 
Machine learning algorithms were employed to determine real-time optimal solutions for a network queuing model, aiming to minimize overall response times as load fluctuated across multiple workflows through composite applications built with Web Services in a Service-Oriented Architecture (SOA).