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Senior Lead - AI Engineer - LLM's and Agents
RES
Source: Headcount Labs · original listing ↗
Job data: Headcount Labs, ODbL 1.0. Job descriptions belong to their employers.
Posted Oct 26
About this role
Senior Lead - AI Engineer 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 engineer the future of AI at one of the world’s leading renewable energy businesses? At RES, we are looking for a Senior AI Engineer to design, build and run the LLM and agent services on our global data and AI platform. This is a hands-on role: you will spend at least 70% of your time building. You will create LLM and RAG services, agents and multi-agent workflows that work over RES’s finance, construction, project, development and HR data. You will also set the AI engineering standards and patterns the rest of the team uses. Reporting to the Head of Data and AI Analytics, you will work closely with our data engineers, modellers and analysts on how AI uses enterprise data. Our Platform Operations team handles day-to-day platform running and infrastructure, so you can focus on building AI. Example use cases • Agents that answer questions over finance, construction and project data in Microsoft Fabric • Document AI for contracts, project documents and reports • AI assistants built into Power BI reporting and business workflows Key Accountabilities • Build AI services: Design, code and deploy LLM and RAG services, agents and multi-agent workflows, from prototype to production. • Evaluation and testing: Set up automated evaluation, testing for non-deterministic outputs, and quality gates before release. • AI architecture and patterns: Define reference designs and reusable patterns for generative AI, agents and retrieval, aligned with our data platform standards. • Data for AI: Work with our modellers and engineers so enterprise data is structured for AI agents to search, retrieve and reason over reliably. • Engineering standards: Set coding standards for Python and LLM orchestration, review code, and build reusable code repositories for the team. • Cost efficiency: Design for efficient token use, semantic caching and compute cost control. • Security by design: Work with Cyber Security and the Data and AI Governance Lead to build access controls (RBAC), data privacy and prompt-injection defences into all agents. • New technology: Evaluate and prototype emerging AI technologies, such as MCP servers, vector databases, knowledge graphs, small and open models, and multimodal AI, and bring proven ones into use. • Coaching and advice: Coach team members and graduates on AI engineering, and advise stakeholders on AI options and trade-offs. Experience Required • LLM and AI engineering: Hands-on experience building LLM solutions in Python, including RAG and agent frameworks (for example LangGraph, LangChain, Semantic Kernel or AutoGen). Experience taking AI into production is an advantage. • Software engineering: Clean, modular, tested code in Python and SQL; building and consuming REST APIs; version control and CI/CD. • AI cloud services: Experience with Azure AI Foundry, Azure OpenAI and Azure AI Search, or equivalent services on another enterprise cloud. • LLM evaluation and operations: Testing, monitoring and improving LLM quality, latency and cost. • AI security: Understanding of security for LLM and agent systems, including prompt injection, data access and guardrails. • Engineering judgement: Understanding of the limits of generative AI, and good judgement on when to use AI and when to use deterministic code or rules. • Communication: Able to explain technical options clearly, run workshops and work with business stakeholders. • Education: Bachelor’s degree in computer science, software engineering, data science or a related field, or equivalent hands-on experience. Desirable • Machine learning and MLOps: model deployment, monitoring and retraining • Microsoft Fabric, Power BI or Databricks • Knowledge graphs or semantic models • Responsible AI frameworks (ISO/IEC 42001, NIST AI RMF, EU AI Act) Why RES? • A global remit: Build AI used across a worldwide renewable energy business. • Real impact: The agents you build will support business decisions across renewable energy projects worldwide. • A modern, cloud-first stack: Azure, Microsoft Fabric, Purview, MCP servers and significant innovation investment. • A collaborative, growing team: Join an experienced data and AI function with scope to shape how AI develops at RES. • Agentic AI: Set the blueprint for how RES builds and runs AI agents.