Data & ML Architect - Newcastle

Role: Data & ML Architect Location: Newcastle Upon Tyne Levels: Associate Manager

Please Note:  Due to the nature of client work you will be undertaking, you will need to be willing to go through a Security Clearance (Including BPSS) process as part of this role, which requires 5+ years UK address history at the point of application.

Hybrid Working: Please note - This role will require you to work from our Newcastle, Cobalt Business Park office for a minimum of 3 days per week

Accenture is a leading global professional services company, providing a broad range of services in strategy and consulting, interactive, technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets and next-generation technology to each business challenge.

As a team:

Our Advanced Technology Centre is a thriving technology and innovation hub from where we deliver high quality services to a number of private and public sector clients.

You'll learn, grow and advance in an innovative culture that thrives on shared success, diverse ways of thinking and enables boundaryless opportunities that can drive your career in new and exciting ways

If you’re looking for a challenging career working in a vibrant environment with access to training and a global network of experts, this could be the role for you. As part of our global team, you'll be working with cutting-edge technologies and will have the opportunity to develop a wide range of new skills on the job. 

Role Overview

As a Data & ML Architect, you will lead the design and architecture of scalable, enterprise-grade data platforms and advanced machine learning solutions. You will define and drive data and AI strategies, architect intelligent data products, and ensure alignment with business objectives. Leveraging modern cloud, AI, and ML technologies, you will deliver robust, secure, and future-proof data architectures that enable business transformation.

Key Responsibilities

Strategic Leadership:

• Set the vision and direction for data and AI architecture for project and client.

• Influence enterprise data and AI strategies, driving adoption of best practices in data, ML, and AI architecture.

• Mentor and develop junior architects and engineers, fostering a culture of technical excellence and innovation.

Architectural Excellence:

• Architect and design enterprise-scale data platforms and ML/AI solutions for scalability, reliability, and performance.

• Define and enforce data and ML architecture standards, patterns, and governance frameworks for data pipelines, ML workflows, and analytics platforms.

• Lead solution design for integrating data from diverse sources (streaming, batch, cloud, on-premises) using Azure, AWS, and GCP services.

AI & ML Leadership

• Architect advanced Data Science, ML and AI solutions, including NLP, RAG LLM and Agentic AI systems.

• Oversee the implementation of MLOps frameworks, ensuring robust deployment, monitoring, and lifecycle management of ML models.

• Evaluate and recommend emerging AI/ML technologies and platforms, driving innovation in intelligent data products.

• Provide Ethical AI leadership to ensure intelligent solutions are implemented when required and in safe methods.

Technical Leadership:

• Oversee data modelling (e.g. medallion architecture), data governance, and quality frameworks. Implementing technical reviews and driving high standards.

• Collaborate with engineering and DevOps teams to establish CI/CD and MLOps architectures using Azure DevOps, Kubernetes, and Terraform.

• Guide the selection and implementation of visualization and analytics tools (Power BI, Tableau, PowerApps).

Stakeholder Engagement:

• Engage with senior stakeholders to translate complex business requirements into technical solutions and strategic roadmaps.

• Ensure all solutions meet security, compliance, and performance standards, and align with enterprise architecture principles.

Innovation & Emerging Technologies:

• Apply modern data and AI architecture patterns (Cloud Native, Steaming/batch ingestion, Agentic AI Design) and leverage platforms such as AI/ML, Big Data, and Data Lakehouse.