Energy Exemplar - Director Cloud Architecture & Delivery
Requirements
• 12+ years in enterprise technology, including 7+ years designing and delivering production cloud architectures for enterprise or regulated customers in large or mid-sized organizations. • Experience leading engineers embedded in customer implementations (forward-deployed, field engineering, or customer-embedded delivery teams), taking AI and data solutions from use case to production while managing customer expectations, scope, and handover. • Strong customer-facing pre-sales experience supporting complex enterprise software opportunities, including technical discovery, solution validation and RFI or RFP responses. • Deep expertise in at least one hyperscaler (Azure preferred) and working fluency across AWS and GCP: compute (VMs, Kubernetes, batch/HPC), storage, networking, identity (Entra ID, SSO/SAML/OIDC, SCIM), and managed data services. • Experience designing AI agent engineering in production, including LLM application and agent orchestration frameworks, retrieval and tool integration, evaluation and monitoring, and model governance. • Ability to connect architecture decisions to customer business objectives, commercial value and delivery feasibility, including cost and risk considerations. • Strong understanding of enterprise integrations, REST/GraphQL, APIs and data flows, alongside cloud performance, scalability, governance and cost optimization. • Executive presence, communication and the ability to lead architecture and risk conversations with CIOs, CISOs, enterprise architects, and procurement at utilities, ISOs/RTOs, and developers. • A Bachelor’s degree in Computer Science, Software Engineering, Computer or Electrical Engineering, Information Systems, or a related technical field. • Master's degree in a technical or business discipline (MS, MEng, MBA) or equivalent demonstrated senior-level experience. • Current professional-level cloud architecture certification: Microsoft Certified: Azure Solutions Architect Expert, AWS Certified Solutions Architect – Professional, or Google Professional Cloud Architect. • Working knowledge of the North American power sector: ISO/RTO market structures (PJM, MISO, ERCOT, CAISO, SPP, NYISO, ISO-NE), utility IRP and transmission planning cycles, and the data-governance expectations that come with CEII and market-sensitive data. • Direct experience with energy market simulation, production cost, capacity expansion, or resource adequacy tools (PLEXOS, Aurora, PROMOD, GridView, EnCompass, or similar), or with ETRM platforms such as Adapt2. • Experience migrating on-premises, compute-intensive analytical software to cloud or SaaS delivery, including customer change management and license-model transitions. • Experience building or scaling a pre-sales architecture capability and supporting cloud adoption across enterprise customers. • Measures of Success • Measures of Success • Strong technical win rates and effective resolution of architecture and security objections in supported opportunities. • Proofs of concept meet agreed success criteria and help customers make informed decisions. • Proposed solutions are feasible, well documented and handed over clearly to delivery teams. • Reusable architectures and enablement improve pre-sales efficiency and technical capability.
Responsibilities
• Build and lead a forward-deployed engineering capability that works closely with customer technical and business teams to translate priority use cases into deployed solutions. • Embed with customer teams, on site and in their cloud tenants, to design, integrate, and deploy AI agents and agentic workflows built on PLEXOS Intelligence, including integration with customer data, enterprise systems, APIs, and Energy Exemplar products. • Act as the senior cloud and AI technical advisor to customers and prospects (utilities, ISOs/RTOs, IPPs, and consultancies), engaging CIOs, CTOs, CISOs, enterprise architects, and IT leaders to build confidence and resolve technical objections. • Partner with Sales and Pre Sales on technical deal strategy, architecture workshops, and demonstrations; scope and guide proofs of concept and pilots with clear success criteria, from environment build and data onboarding through executive readout. • Define acceptance criteria with customers and validate deployed agents and solutions against the agreed success criteria (accuracy, performance, security, and human-in-the-loop controls), with evidence customers can take to their governance and risk teams. • Own the cloud reference architectures for PLEXOS Cloud, PLEXOS Intelligence, and adjacent AI agents and products as deployed for customers: landing zones, identity and access, networking, data residency, high-performance compute for large nodal and stochastic runs, and cost models. • Lead the pre-sales-to-Services handoff for cloud and AI deals: deployment plan, architecture decision record, risk register, and onboarding timeline agreed with Post Sales Services before contract signature. Develop migration roadmaps and delivery estimates with Services and Engineering, documenting assumptions, risks, and responsibilities before commitments are made, and provide architecture oversight for complex implementations so the proposed solution carries through to delivery. • Set guardrails for agentic deployments in regulated environments: agent identity and permissions, tool-use boundaries, evaluation harnesses, observability, and audit trails. • Lead cloud and AI architecture input into RFIs, RFPs, security reviews, vendor risk assessments, and proposals, coordinating specialist input and ensuring responses are accurate and achievable; lead customer discussions on SOC 2, ISO 27001, NERC CIP adjacency, FedRAMP-equivalent expectations, and CEII handling, and maintain a current response library with Information Security. • Establish reusable discovery frameworks, technical materials, agent templates, and deployment patterns (SaaS, customer-tenant, hybrid), including integration with customer data platforms (Databricks, Snowflake, Fabric, on-prem SQL) and API/automation patterns for model orchestration, plus runbooks and environment-as-code templates that make repeatable launches the default. • Partner with Product Engineering to turn repeatable customer implementation patterns into supported product capabilities, reusable integrations, and deployment assets.
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