KPMGTrustedAIservicescan help with designing, building, deploying, and using AI tech solutions in a responsible and ethical manner, seeking to accelerate value with confidence. Analysts recognize that KPMG is an industry leader in AI, machine learning, data analytics, cyber security and risk.
The Azure Rapid Prototype Engineer will sit within the Digital Build Service Team in Global Lighthouse and will work closely with the Azure Tech Lead and an Azure AI Modeler to build global client solutions to enable KPMG’s Connected, Powered, Trusted and Elevate propositions. Solutions built by the Digital Build Service will be primarily AI focused, with remaining solutions covering data, analytics and emerging technology.
Responsibilities:
Develop applications using Azure APIs and SDKs in Python, .NET, Java and other supported development languages.
Work with senior staff within the Digital Build Service and across KPMG Global to gather requirements / understand business purpose of solutions
Design and develop Azure-based solutions for global member firms (to provide to clients), with an initial focus on Connected solutions
Design, develop, enhance, and deploy scalable AI solutions on various cloud platforms (both front end and back end), focusing on services like Vertex AI, Azure AI
Understand and implement modern application development methodologies like microservices (including dockers & Kubernetes) and serverless architectures on Azure.
Implement front end development with Django, Flask and Streamlit and able to translate the backend into an easy to use web interface
Develop and implement Azure-based prototypes
Deploying machine learning models in production using Azure Machine Learning Service.
Knowledge of MLOps practices and integrating them with traditional DevOps pipelines.
Automate deployments, testing, and configuration management using tools like Azure DevOps, GitHub, and Ansible.
Test and validate Azure-based solutions
Explore and implement innovative AI technologies and tools like Prompt flow to enhance user interaction and experience.
Ensure the integration of security practices throughout the AI lifecycle, adopting a DevSecOps mindset to safeguard applications and data
Analyse and improve the efficiency, scalability, and stability of various deployed systems, leveraging your expertise in containers and cloud-native technologies.
Provide technical support to / respond to queries from global member firms on solutions.