Job Summary

Role: AI Enterprise Architect The AI Enterprise Architect defines the target-state AI architecture and roadmap to enable scalable, secure, and governed AI adoption across the enterprise. This is a senior leadership role responsible for shaping AI strategy into execution, guiding platform and vendor choices, and establishing architectural governance—ensuring alignment with business priorities, enterprise standards, and regulatory requirements. Key Responsibilities Define and evolve the enterprise AI reference architecture and implementation roadmap, spanning LLM platforms, data pipeli

Key Responsibilities

Key Responsibilities

• Define and evolve the enterprise AI reference architecture and implementation roadmap, spanning LLM platforms, data pipelines, knowledge graphs, machine learning models, APIs, microservices, and event-driven architectures within AWS.
• Design and govern complex, multi-agent AI workflows capable of reasoning, planning, and autonomous execution, with strong state management and guardrails.
• Establish and embed robust evaluation frameworks to measure performance, reliability, and quality across both Generative AI and traditional ML models.
• Provide architectural leadership across delivery teams, ensuring consistent adoption of standards, patterns, and best practices for AI solutions.
• Enable seamless integration of AI capabilities into enterprise platforms and business processes, driving reusable services, accelerators, and scalable design patterns.
• Lead architecture governance forums, design authorities, and executive-level discussions to drive alignment and decision-making.
• Act as a trusted advisor to senior stakeholders on AI strategy, risk, and implementation approaches.

Skill Requirements

Key Skills & Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related discipline.
• Proven, hands-on experience delivering large-scale AI solutions, spanning both:
• Traditional Machine Learning (e.g., predictive analytics, forecasting, deterministic models)
• Generative AI (e.g., LLMs, knowledge graphs, agentic AI, orchestration frameworks)
• Strong expertise in AWS cloud, including services such as Amazon Bedrock, and modern cloud-native architectures (APIs, microservices, event-driven systems).
• Experience with Databricks (Lakehouse, Unity Catalog) and traditional ML techniques is highly desirable, enabling hybrid AI architectures.
• Demonstrated experience in enterprise, solution, or data architecture roles within complex organisations.
• Strong understanding of enterprise architecture frameworks (e.g., TOGAF) and governance models.
• Experience operating within regulated environments, with knowledge of data protection, AI ethics, and compliance considerations.
• Excellent leadership, communication, and stakeholder management skills, with the ability to influence at executive level.
• Proven ability to define and deliver enterprise-wide technology strategy and governance frameworks.

Other Requirements

Desirable

• Experience within the Energy & Utilities sector.
• Familiarity with industry-specific AI use cases, platforms, and regulatory considerations.