Digital Product Engineer

Job Purpose
This is a senior technical leadership position sitting at the intersection of AI platform strategy, enterprise architecture, delivery execution, and NGV business value. You will own and evolve NGV's AI, data, and digital platform product portfolio — translating business priorities into secure, scalable, and reusable platform capabilities across Generation, Competitive Transmission, Asset Development, large-load growth, and emerging business models.

Key Accountabilities
AI Platform Strategy & Product Roadmap

Define and own the product vision, roadmap, and investment narrative for NGV's AI, data, and digital platforms
Translate NGV business priorities into a sequenced portfolio of platform capabilities balancing delivery needs with scale
Drive reusable platform capabilities — build once, scale across business units, regions, and repeatable use cases
Technical Product Ownership

Own technical product definition covering agentic workflows, RAG/GraphRAG, vector search, MCP/API integrations, model governance, and AI output quality controls
Shape Azure-native designs (Azure OpenAI, AI Search, Document Intelligence, Cosmos DB, Container Apps/Kubernetes, Entra ID, API Management) with architecture and engineering teams
Ensure solutions are secure, scalable, cost-aware, and aligned to National Grid architecture, cloud, security, and AI standards
Data, Integration & Architecture Alignment

Define integration patterns for enterprise data sources including Maximo, SAP, Primavera P6, SharePoint, Cognite, ISO/RTO feeds, and third-party data providers
Own decisions around build vs. buy vs. configure, ETL/ELT pipelines, data quality, metadata, lineage, and API design
Senior Stakeholder Engagement & Delivery

Act as the senior product interface with NGV leaders across Generation, Transmission, Asset Development, commercial, regulatory, and operations
Lead products through discovery, MVP, scale, operate, and optimize — with clear scope, acceptance criteria, release plan, and value measures
Define success metrics (adoption, time saved, accuracy, risk reduction, revenue enablement) with 30/60/90-day tracking and evidence-based reporting
Governance, Risk & Responsible AI

Embed RBAC, encryption, data classification, model cards, human oversight, audit logs, and responsible AI controls into every product
Support governance forums, investment cases, architecture submissions, and executive reviews with clear value, cost, risk, and delivery trade-offs

Qualifications
Significant experience in senior digital product management, AI/data/platform delivery, or technology-enabled transformation in complex enterprise environments
Proven track record owning AI, data, cloud, or digital platform products from discovery through MVP, scale, and continuous improvement
Deep practical knowledge of Azure-native patterns: identity, access, networking, AI services, data stores, observability, CI/CD, and infrastructure-as-code
Hands-on knowledge of enterprise AI: LLM applications, RAG/GraphRAG, vector search, AI agents, prompt engineering, output evaluation, and model governance
Strong understanding of data engineering patterns including ETL/ELT, APIs, data quality, metadata, lineage, master data, and secure enterprise data access
Ability to translate complex technical trade-offs into business-relevant choices, risks, costs, and decisions for senior leaders
Preferred

Domain knowledge in energy, utilities, generation, competitive transmission, asset development, large-load/data-centre demand, or RTO/ISO markets
Familiarity with Maximo, SAP, Primavera P6, SharePoint, OpenText/Autodesk, Cognite, or similar enterprise platforms
Experience with platform resilience: multi-AZ/region, autoscaling, SLA/SLO definition, performance testing, and cost optimization
Experience with AI governance tooling such as Microsoft Purview, Langfuse, OpenTelemetry, or equivalent LLM observability platforms