AI Solution Architect

• Owns end-to-end outcomes to translate validated AI opportunities into reliable, trusted AI products in production that deliver sustained business value at scale through direct hands-on engineering leadership and expert technical contribution.
• Ensures AI solutions are production-ready and fit for business use, covering security, explainability, observability, performance, and ongoing reliability beyond experimentation.
• Operates at the intersection of AI product delivery, AI engineering/MLOps platforms, and Responsible AI governance, enabling efficient and trustworthy progression from exploration to production.
• Provides the engineering backbone to scale AI consistently across domains, embedding value realization, risk management, and operational integration from the start.

Business Complexity / Context

• Owns the AI Engineering and MLOps capability within Data & Analytics, setting principles, standards, and best practices for IT and the wider community.
• Acts as AI Engineering solution authority for new AI initiatives, ensuring designs are production-ready, scalable, and aligned with enterprise architecture, security, and Responsible AI standards, leading by example through direct technical contribution.
• Accountable for a reliable and future-proof AI platform, ensuring continuous operability, performance, and evolution in line with business needs and technology developments.
• Leads the AI Engineering practice, guiding and coaching AI and ML engineers across global and decentralized teams; holds line management responsibility for a small core team and designs, builds, and reviews critical AI components and pipelines., * Designs, engineers, and troubleshoots complex and business-critical AI components, reference architectures, and production pipelines, especially for complex or first-of-kind solutions.
• Delivers and scales AI Engineering and MLOps capabilities that enable reliable, secure, and scalable AI products to move from validated use cases into production across domains.
• Owns engineering quality and production readiness, ensuring AI solutions meet enterprise standards for reliability, performance, security, and compliance.
• Translates value levers into engineered outcomes by shaping AI initiatives into measurable product goals
• Leads the AI Engineering practice, combining hands-on technical contribution with guidance and line management of the core AI Engineering team.

1. Guidance

• Owns the AI Engineering and MLOps capability within Data & Analytics, providing clear technical direction and driving tactical and operational execution.
• Ensures end-to-end alignment across platform, engineering, and business priorities through close collaboration with Data Engineering, Platform Engineering, and domain teams.
• Prepares decision briefs and secures approvals through established governance forums, reporting progress, risks, and outcomes to D&A leadership.

1. Innovation & Solution Management

• Translates strategy into production-ready designs, execution plans, and working solutions.
• Leads hands-on first-time delivery of new AI solutions, establishing reusable engineering patterns for scale.
• Drives continuous improvement and selective innovation, applying new technologies where they demonstrably deliver business value.
• Stay ahead of emerging technologies and industry practices, while producing thought leadership position papers and selectively introducing innovation that creates measurable business value.

1. Strategy & Planning

• Defines the vision and roadmap for the AI Engineering and MLOps capability within Global IT, Data & Analytics.
• Translates value levers into engineered outcomes by shaping AI initiatives into measurable product goals
• Ensures the AI platform remains performant, secure, and architecturally compliant, in collaboration with relevant capabilities and strategic partners.

• Master’s degree in Computer Science or similar, with 10+ years of experience in Data & Analytics, including AI Engineering, MLOps, and platform engineering.
• Deep hands-on expertise in designing, building, and productionizing AI and advanced analytics solutions at scale.
• Proven experience defining and executing technology roadmaps and evolving enterprise-grade AI platforms.
• Strong technical background on Azure data and AI platforms, including Databricks (Lakehouse), Azure Data Factory, ADLS, Azure Functions, and CI/CD with Azure DevOps.
• Demonstrable hands-on experience with MLOps on Azure, including infrastructure, security, logging and monitoring, pipeline orchestration, data quality, and model observability.
• Solid understanding of Agile and DevOps ways of working, combined with experience in innovation, experimentation, and solution design.

1. Skills

• Hands on technical problem ownership: able to diagnose complex engineering issues, design and implement robust solutions, and resolve production challenges end to end.
• Strategic and product oriented mindset: makes and implements technical choices that connect engineering decisions to long term objectives and business outcomes.
• Change and stakeholder leadership: able to influence, align, and drive adoption across teams and domains through clear communication and technical credibility.
• Innovation leadership: prototypes and engineers ideas into working solutions, managing technical risk while enabling experimentation and learning

1. Attitudes

• Collaborative and inspiring leader who sets the technical bar through hands on contribution and leads by example
• Confident communicator
• Strong focus on delivery and are passionate about creating excellent products and services that meet user needs
• Your enthusiasm will help you collaborate with, and inspire an expert team