Artificial Intelligence Engineer

• Machine Learning Operations (MLOps) and AI Engineering Capability Lead
• 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 / Con
• 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.
• Areas of responsibi
• Data & Analytics: Enterprise AI Engineering Capability (AI Products, MLOps, Innova

Main Accountabilities /Key Tasks: 1.

• Scope 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.
• 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 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 planning.
• Defines the vision and roadmap for the AI Engineering and MLOps capability within Global IT, Data & A
• 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.
• Critical Com
• Knowledge 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 productionising 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 Dev Ops.
• 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 Dev Ops ways of working, combined with experience in innovation, experimentation, and solutions.

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