Senior AI Architect and Engineer Lead

Senior AI Architect and Engineer Lead   Description   Do you want to work to make Power for Good?   We're the world's largest independent renewable energy company, guided by a simple yet powerful vision to create a future where everyone has access to affordable, zero carbon energy.   We know that achieving our ambitions would be impossible without our people. Because we're tackling some of the world's toughest problems, we need the very best people to help us. They're our most important asset so that's why we continually invest in them.   RES is a family with a diverse workforce, and we are dedicated to the personal and professional growth of our people, no matter what stage of their careers they're at. We can promise you rewarding work which makes a real impact, the chance to learn from inspiring colleagues from across a growing, global network and opportunities to grow personally and professionally.   Our competitive package offers a wide range of benefits and rewards.   Locations: UK - Kings Langley or Glasgow. Spain - Albacete or Madrid.   Summary / Purpose of the Role   Are you ready to architect and engineer the future of data and AI at one of the world's leading renewable energy businesses? At RES, we are looking for a Senior AI Architect and Engineer Lead to set the AI architecture for RES’s data and AI platform, and to design, engineer and build the AI services that run on it: LLM and generative AI solutions, agents, machine learning and predictive models, and the next generation of data and AI technology.   Reporting to the Head of Data and Analytics, you will own the forward-looking strategic and conceptual design of our global platform. This role combines AI and solution architecture with hands-on AI engineering, and works closely with our data architects and engineers on how AI uses enterprise data. You will establish standards optimized for LLM reasoning, and coding multi-agent orchestration frameworks. You will work alongside our established Platform Operations team—who handle the day-to-day platform run, infrastructure maintenance, and legacy migrations—allowing you to serve as the ultimate technical authority for AI innovation for data and analytics.   Key Accountabilities - Own Strategic AI Platform Direction: Define the target-state AI architecture, reference designs and patterns across generative AI, agents, machine learning and retrieval, aligned with the data platform and global business goals. - Design the Data-to-AI Interface: Establish how enterprise data is structurally modelled (using star schemas, semantic layers, dimensional modelling, and knowledge ontologies) to ensure autonomous AI agents can search, retrieve, and reason over data reliably without hallucinating. - Build AI services: Lead the hands-on design, coding and prototyping of AI solutions, including LLM and RAG services, agents and multi-agent workflows, and machine learning and predictive models. Set automated evaluation frameworks, non-deterministic testing standards and compliance gates. - Set Engineering Standards & Code Patterns: Provide senior technical authority in Python, SQL, advanced software engineering, and LLM orchestration to the wider team. Review production code and build reusable code repositories to ensure quality engineering solutions. - Lead future technology: Identify, evaluate and prototype emerging AI and data technologies, such as MCP servers, vector databases, knowledge graphs, small and open models, and multimodal and document AI, and bring proven ones into RES’s data and AI portfolio. - Architect Financial Efficiency: Design cloud data and AI patterns for optimal consumption efficiency, establishing best practices for token management, semantic caching, and compute cost optimization. - Enforce Trust & Security by Design: Partner with Cyber Security and the Data & AI Governance Lead to ensure data privacy, access boundaries (RBAC), and prompt-injection defence mechanisms are embedded across all agents. - Influence Global Leadership: Advise senior IT executives and business leaders on complex technical priorities, helping guide investment choices around data architecture, software engineering, and AI automation.   Experience Required - AI engineering: Hands-on experience building and testing AI solutions in Python and SQL, across LLMs and generative AI, RAG, agent frameworks (for example LangGraph, AutoGen, Semantic Kernel or LangChain) and machine learning. Experience taking AI into production is an advantage. - AI architecture: Experience setting AI and solution architecture, reference designs and engineering standards. - AI tooling: Experience with Azure AI Foundry, Azure OpenAI and Azure AI Search, or equivalent AI services on another enterprise cloud. - Enterprise Platform Design: 7+ years of demonstrated track record designing enterprise data architectures and data warehouses at scale to create organizational efficiency. - Strategic & Conceptual Direction: Deep experience setting architectural reference designs, modeling standards, and technical direction across modern cloud data estates. - Hands-on AI & Software Engineering: Proven, deep experience writing clean, modular code in Python, SQL, and consuming complex REST APIs. Must have hands-on experience building, testing, and deploying custom LLM agent frameworks (e.g., LangGraph, AutoGen, or LangChain) in production. - Modern Cloud Data Tooling: Strong technical experience with Azure data and AI ecosystems (Azure AI Foundry, Microsoft Fabric, Azure AI Search) or equivalent components on alternative enterprise cloud platforms. - Data Governance & Security Knowledge: Solid understanding of how data cataloging, lineage, and privacy compliance integrate with autonomous AI systems. - Executive Communication & Leadership: Demonstrated ability to articulate complex technical strategies, lead workshops, run steering groups, and influence senior executive stakeholders. - Exceptional Engineering Judgment: A proven track record of understanding the limitations of generative AI, demonstrating high judgment on where AI automation adds value versus where deterministic engineering must be used. - Educational Foundation: Bachelor's degree in computer science, software engineering, data analytics, or equivalent deep, hands-on senior career experience. - MLOps and LLMOps: model deployment, monitoring and retraining. - Responsible AI frameworks (ISO/IEC 42001, NIST AI RMF, EU AI Act). Desirable - Enterprise data architecture, data warehousing or semantic modelling (star schemas, dimensional models). - Microsoft Fabric, Azure data services or Databricks. Why RES? - A genuinely global remit: You will set the architecture and build the AI for a data platform used across a worldwide renewable energy business. - Work at the intersection where data, analytics and AI meet: Your architecture and the agents you build will drive business outcomes across the globe in renewable energy. - A modern, cloud-first stack: Azure, Fabric, Purview, MCP servers, and significant innovation investment. - A collaborative, growing data and analytics function: Scope to shape how analytics and AI evolves for data and platforms. - Join a brand new, agile team: Be at the heart of AI, innovation, Next Gen technology, and driving competitive advantage for RES. - Agentic AI leadership: Set the blueprint for how RES builds and operates autonomous agents on our platform.