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 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.
Key Skills & Experience
* Bachelor’s or Master’s degree in Computer Science, Data Science, or a related discipline.
* 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.
* Experience within the Energy & Utilities sector.
* Familiarity with industry‑specific AI use cases, platforms, and regulatory considerations.
#J-18808-Ljbffr