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Listing evidence and provenance
- Source
- Workday View original listing
- Freshness
- Open in the current public read path; this is not an employer guarantee.Last observed October 9, 2026
- Classification
- Modern power-system classification not yet published for this listing.Legacy source industry field: Utilities
- Pay provenance
- Employer-provided pay
Job Description
Connected, Committed, Trustworthy, Safe
Requirements
- Seven years of related functional experience.
- Bachelor's degree in Technology, Science, Business or related field, or 4 years of experience equivalent to the position.
- Exposure to diverse systems, technologies and processing environments in a large Corporate setting.
- Project solution architecture experience.
- Knowledge of core aspects of architecture (Software, Hardware, Network, Integration, Data, hosting models, platform types, etc.).
- Strong analytical, problem-solving and troubleshooting skills.
- Extensive knowledge of future technology trends within area of expertise.
- Excellent communication skills, effective with varying organizational levels and skill sets.
- Excellent Relationship Management and collaboration skills, with a track record of working as one team cross-organizationally to drive innovation and business results.
- Experience architecting and governing Microsoft Copilot and enterprise AI solutions, including AI control planes, enterprise data access, security, governance, and AI-ready data products.
- Working experience designing cloud-based AI and machine learning solutions, supported by strong cloud solution architecture fundamentals.
- Experience with generative AI and predictive machine learning patterns, including retrieval-augmented generation (RAG), agents, model serving, evaluation, and monitoring.
- Experience with MLOps practices, including model lifecycle management, experimentation, deployment, observability, performance monitoring, and operational transition.
- Experience with GCP, BigQuery, and Databricks for AI, machine learning, analytics, or data engineering solutions.
- Experience creating proofs of concept, performing technical validation, and using limited coding to test architecture decisions and reduce delivery risk.
- Experience defining measurable solution success criteria, evaluating model and solution quality, validating business and technical fit, and supporting build-versus-buy decisions.
- Experience designing AI-related data flows, retrieval patterns, vector stores, feature pipelines, metadata, lineage, and governed access in collaboration with data architects.
- Working knowledge of responsible AI, cybersecurity, privacy, data governance, and model risk practices, including identifying risks, incorporating established controls, and coordinating required reviews.
- Experience defining nonfunctional requirements, deployment patterns, scalability, latency, resiliency, observability, model monitoring, production-readiness criteria, and support expectations.
- Experience collaborating with engineering and FinOps on model and service selection, consumption forecasting, performance, scalability, and cost optimization.
- Ability to translate business needs provided through product owners and business analysts into architecture requirements, evaluation criteria, and implementable solution designs.
- Experience documenting reusable AI architecture patterns, capturing lessons learned, and recommending improvements to standards and reference architectures.
- Relevant GCP, Databricks, cloud architecture, machine learning, or AI certifications are preferred but not required.
- Our culture is grounded in shared values and employee commitments that guide how we work, support one another, and grow together.
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