AI Engineering & Platform Leadership Senior Manager - Utilities

Accenture•Kirkland, WA

1d•$112,900 - $338,300•Hybrid

About The Position

Accenture's AI and Data practice operates at the intersection of deep industry knowledge and applied AI and data engineering. We assist leading Resources and Utilities organizations in reinventing their operations by designing data foundations, AI platforms, and governance models that transform data into trusted, production-grade intelligence. We are seeking practitioners who understand enterprise operations, identify friction points, and engineer AI and data solutions to fundamentally transform business processes and outcomes, rather than generalists who advise on AI in the abstract.

Requirements

Minimum of 10 years of experience in software or AI/ML engineering, including architecture leadership.
Minimum of 6 years of experience architecting and delivering enterprise AI/LLM platforms across major cloud and model providers.
Minimum of 3 years of experience with LLMOps/MLOps at scale and multi-cloud architecture (AWS, Azure, Google).
Minimum of 5 years of experience leading engineering teams and end-to-end solution delivery.
Minimum of 3 years of experience with AI security, governance, and cost optimization (FinOps for AI).
Minimum of 1 year of experience serving utilities clients (electric, gas, or water) or in a utilities finance, controllership, or regulatory function.
Bachelor's degree or equivalent (minimum 12 years' work experience). If Associate’s Degree, must have equivalent minimum 6-year work experience.

Nice To Haves

Master’s degree in a relevant field.
Cloud architecture or AI engineering certifications (AWS, Azure, or Google).
Demonstrated thought leadership or published innovation work in enterprise AI.

Responsibilities

Architect enterprise AI platforms, leading the architecture and delivery of platforms spanning multiple model providers (OpenAI, Anthropic) and cloud ecosystems (AWS, Azure, Google).
Set technical standards, defining reference architectures and technical standards for LLMOps, security, reliability, and cost governance across the AI estate.
Lead engineering teams at scale, overseeing multiple teams delivering AI solutions, providing technical direction, coaching, and quality assurance.
Own model strategy, serving as a senior technical authority on hyperscaler AI services and model selection, guiding enterprise-grade deployment of generative and agentic AI.
Drive cost and security governance, establishing FinOps-for-AI cost optimization practices and embedding AI security and governance into platform design.
Shape solutions and growth, partnering with clients and pursuit teams to shape solutions, architectures, and delivery models for major AI programs.