Senior AI Architect and Engineer Lead

RES

About The Position

RES is seeking a Senior AI Architect and Engineer Lead to set the AI architecture for its data and AI platform and design, engineer, and build AI services, including LLM and generative AI solutions, agents, machine-learning and predictive models, and emerging data and AI technologies. Reporting to the Head of Data and Analytics, this role owns the strategic and conceptual design of the global platform and combines AI and solution architecture with hands-on engineering. The role works closely with data architects and engineers to define how AI uses enterprise data, establish standards optimized for LLM reasoning, and develop multi-agent orchestration frameworks. The Platform Operations team handles day-to-day platform operations, infrastructure maintenance, and legacy migrations, while this role serves as the technical authority for AI innovation in data and analytics. The position has a global remit supporting RES’s renewable energy business. It will guide platform architecture and engineering, shape the use of autonomous agents, and advise senior leaders on technical priorities and investment. RES describes its workplace as a diverse, growing global network and offers opportunities for personal and professional growth.

Requirements

At least 7 years of demonstrated experience designing enterprise data architectures and data warehouses at scale.
Deep experience setting architectural reference designs, modelling standards, and technical direction across modern cloud data estates.
Hands-on experience building and testing AI solutions in Python and SQL across LLMs and generative AI, RAG, agent frameworks, and machine learning.
Proven, deep experience writing clean, modular Python and SQL code and consuming complex REST APIs.
Hands-on experience building, testing, and deploying custom LLM agent frameworks, such as LangGraph, AutoGen, or LangChain, in production.
Experience setting AI and solution architecture, reference designs, and engineering standards.
Strong technical experience with Azure data and AI ecosystems, including Azure AI Foundry, Microsoft Fabric, and Azure AI Search, or equivalent components on another enterprise cloud platform.
Solid understanding of how data cataloguing, lineage, and privacy compliance integrate with autonomous AI systems.
Demonstrated ability to articulate complex technical strategies, lead workshops, run steering groups, and influence senior executive stakeholders.
Strong engineering judgment about generative AI limitations and when AI automation is valuable versus when deterministic engineering should be used.
Bachelor’s degree in computer science, software engineering, or data analytics, or equivalent deep, hands-on senior career experience.

Nice To Haves

Experience taking AI solutions into production.
Experience with Azure AI Foundry, Azure OpenAI, and Azure AI Search, or equivalent AI services on another enterprise cloud.
Experience with MLOps and LLMOps, including model deployment, monitoring, and retraining.
Knowledge of responsible AI frameworks such as ISO/IEC 42001, NIST AI RMF, and the EU AI Act.
Experience in enterprise data architecture, data warehousing, or semantic modelling, including star schemas and dimensional models.
Experience with Microsoft Fabric, Azure data services, or Databricks.

Responsibilities

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.
Establish how enterprise data is modelled—including star schemas, semantic layers, dimensional modelling, and knowledge ontologies—so autonomous AI agents can search, retrieve, and reason over data reliably.
Lead hands-on design, coding, and prototyping of AI solutions, including LLM and RAG services, agents, multi-agent workflows, machine-learning models, and predictive models.
Set automated evaluation frameworks, non-deterministic testing standards, and compliance gates.
Provide senior technical authority in Python, SQL, software engineering, and LLM orchestration; review production code and build reusable code repositories.
Identify, evaluate, and prototype emerging AI and data technologies, and incorporate proven technologies into RES’s data and AI portfolio.
Design cloud data and AI patterns for efficient consumption, including best practices for token management, semantic caching, and compute-cost optimization.
Work with Cyber Security and the Data & AI Governance Lead to embed data privacy, role-based access boundaries, and prompt-injection defences across agents.
Advise senior IT executives and business leaders on technical priorities and investment choices involving data architecture, software engineering, and AI automation.