Lead Engineer, .NET Full Stack Development

Overview In this role you will shape AI-enabled engineering solutions within KPMG’s Tax Ignition team. You’ll translate business needs into scalable AI features and APIs, using modern cloud and microservices patterns. You will build client-facing applications and secure data services, leveraging Azure and leading ML frameworks. You’ll integrate LLMs into front-end and back-end systems while collaborating with product owners and data scientists to deliver real client impact. This is a hands-on, cross-functional role at scale within a respected professional services firm. Compensation / Benefitscomprehensive benefits packagemedical and dental plansvision coveragedisability and life insurance401(k) planswell-being benefits ResponsibilitiesCollaborate with product owners and data scientists to translate business needs into AI-powered features and APIsArchitect, develop, and maintain scalable microservices for model inference (on Azure or similar)Build and maintain client-facing web apps in Angular + TypeScript, Python or .NET Framework/C# backend services and APIs, secure SQL databases (T-SQL, schema design, stored procs)Leverage Azure services (App Services, Functions, SQL Database, DevOps) for deployment, scaling and monitoringIntegrate Large Language Models (LLMs) and other AI services (Azure OpenAI, Custom Models Serving) into our front-end and back-end systems Key requirementsMinimum five years of recent experience in software development and systems architectureBachelor's degree from an accredited college or university in computer science, engineering or related field is preferred; Master's degree from an accredited college or university is desiredSolid understanding of software engineering such as microservices architecture, RESTful APIs, authentication/authorizationExperience building enterprise systems using Python, .NET, C#, Angular, SQL and Azure is preferredStrong communication and analytical skills are required, strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) is preferredstrong communicationanalytical skillsprofessional integritymicroservices architectureRESTful APIsauthentication/authorization