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Greater London, England, United Kingdom
Hybrid
Within ERM, our Capital Project Delivery service line is investing in AI and automation as a strategic enabler of its consulting services.
We are establishing a dedicated AI & Automation Implementation Support role to accelerate this transformation — embedding intelligent tooling into our delivery workflows, driving adoption across the practice, and translating emerging AI capabilities (LLMs, agentic systems, intelligent document processing) into tangible commercial outcomes for clients.
This is a hybrid build-and-enable role. The post-holder will spend part of their time designing and building AI/automation solutions — primarily on the Microsoft Fabric and Power Platform stack — and part of their time driving adoption through training, enablement, and stakeholder engagement across CPD.
What you'll do
Design, build and maintain AI-powered data and document pipelines on Microsoft Fabric (Lakehouse, Delta tables, notebooks, Dataflows Gen2, pipelines) supporting CPD service lines.
Apply Large Language Models (LLMs) to real consulting workflow problems — including document extraction, classification, summarisation, retrieval-augmented generation (RAG), and structured output generation from technical reports.
Develop Intelligent Document Processing (IDP) capabilities for the long-form, table-heavy, multi-format documents that characterise TDD, ESDD, owner's engineering and lender's advisory engagements.
Build Power BI dashboards and semantic models that translate AI/automation outputs into executive-ready insight for internal stakeholders and clients.
Explore and prototype Agentic AI patterns (tool-use agents, planner–executor, multi-agent orchestration) for consulting workflows where they offer step-change value.
Embed Responsible AI practice into every solution — including source citation, verifiable reasoning, output guardrails and alignment with frameworks such as the EU AI Act, ISO/IEC 42001 and the NIST AI RMF.
Drive adoption of AI and automation tooling across CPD through training sessions, workshops, lunch-and-learns and written enablement materials.
Act as a trusted internal advisor to project managers, technical leads and Partners on where AI and automation can credibly improve delivery quality, speed or margin.
Run discovery and requirements-gathering workshops with stakeholders across CPD to identify, scope and prioritise candidate use cases.
Apply a product mindset — defining MVPs, iterating on user feedback, and managing a backlog of CPD AI/automation initiatives.
Contribute to ERM's wider AI Champions network and CPD technical communities, sharing learnings and reusable patterns across the firm.
Retain an active attachment to the TDD subteam, contributing to fee-earning client engagements (renewables, energy infrastructure and adjacent capital projects).
Identify and deliver client-facing AI/automation workstreams within TDD and broader CPD engagements — supporting proposal scoping, pricing inputs and delivery.
Apply a commercial mindset, recognising opportunities to extend or secure billable client work through differentiated AI-enabled delivery.
What we're looking for
Degree (or equivalent) in a sustainability-related, environmental, engineering, scientific or quantitative discipline, with demonstrable analytical and data-driven capability; 1–2+ years of consulting, technical, data or related experience.
Strong hands-on development skills in Python and SQL, with practical experience building data and AI pipelines end-to-end (ingestion → transformation → modelling → delivery).
Working proficiency in Microsoft Fabric — Lakehouse, Delta Lake, notebooks, Dataflows Gen2 and pipelines — in line with ERM's strategic adoption of the integrated Fabric Data Platform.
Strong Power BI skills, including semantic modelling, DAX, and the design of executive-ready dashboards and reports for non-technical audiences.
Demonstrable experience applying LLMs and/or ML to real workflow problems — e.g. document extraction, classification, summarisation, retrieval-augmented generation (RAG), structured output generation.
Familiarity with the wider Microsoft 365 and Power Platform ecosystem — Copilot extensibility, Power Automate, SharePoint/OneDrive, and Microsoft Graph — and how to compose them into low-friction business solutions.
Awareness of Agentic AI concepts (e.g. tool-use agents, planner–executor patterns, multi-agent orchestration) and a clear appetite to develop hands-on agent-building skills within the role.
Product mindset: comfort with requirements gathering, stakeholder engagement, MVP scoping and iterative delivery — whether evidenced through formal training (e.g. a recognised AI/product apprenticeship, ongoing or completed) or applied project experience.
Awareness of AI governance and Responsible AI frameworks (EU AI Act, ISO/IEC 42001, NIST AI RMF or equivalent) and a practical approach to embedding them in delivery — for example, requiring AI systems to cite verbatim sources and explain their reasoning as a baseline observability pattern.
Excellent written and spoken English; ability to translate technical concepts for non-technical audiences, including senior client stakeholders.
Right to work in the UK.
Direct experience in capital-projects, infrastructure, energy or environmental consulting — particularly Technical Due Diligence (TDD), ESDD, owner's engineering or lender's advisory engagements — with an understanding of the document types, deliverable formats and client expectations specific to those workstreams.
Experience designing and delivering training, workshops, lunch-and-learns or enablement programmes to upskill technical and non-technical colleagues on AI/automation tools.
Familiarity with Intelligent Document Processing (IDP) techniques — layout-aware parsing, table extraction, OCR pipelines, and downstream LLM-based structuring of unstructured documents.
Exposure to LLM evaluation and observability practices — e.g. eval harnesses, output guardrails, LLM-as-judge patterns, or production-grade frameworks such as Azure AI Foundry's evaluation suite — with appetite to develop these as a discipline within the role.
Working knowledge of at least one other European language, useful for engaging with EMEA project teams and clients.
Active engagement with internal or external technical communities — e.g. ERM's AI Champions network, internal Teams communities of practice, technical blogs, or open-source contributions in AI/ML.
Current or recent participation in a recognised AI/product apprenticeship or structured programme (e.g. "AI for Business Value" or similar) — including exposure to product management, stakeholder engagement, requirements gathering and AI governance.
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