Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global professional services organization helps businesses, governments, and other organizations build digital capabilities, optimize operations, accelerate growth, and enhance services through technology, cloud, data, artificial intelligence, and industry expertise.
Its AI and Data practice focuses on combining deep industry knowledge with applied AI and data engineering to help Resources and Utilities organizations modernize their operations. The practice develops data foundations, AI platforms, and governance models that enable trusted, production-grade intelligence and support the transformation of business processes and outcomes.
You Are
The organization is seeking a Senior AI Engineering Leader who can architect and deliver enterprise AI platforms across multiple model providers and cloud ecosystems. The successful candidate will establish technical standards for building, securing, and operating generative and agentic AI at scale while leading multiple engineering teams responsible for delivering enterprise AI solutions.
The ideal candidate will serve as a technical authority on hyperscaler AI services, model selection, LLMOps, and enterprise-grade AI deployment, combining strong engineering leadership with practical experience transforming complex business requirements into scalable technology solutions.
The Work
- Architect and deliver enterprise AI platforms spanning multiple model providers, including OpenAI and Anthropic, and cloud ecosystems such as AWS, Azure, and Google.
- Define reference architectures and technical standards for LLMOps, security, reliability, and cost governance across enterprise AI environments.
- Lead multiple engineering teams delivering AI solutions at scale while providing technical direction, coaching, mentorship, and quality assurance.
- Serve as a senior technical authority on hyperscaler AI services and model selection.
- Guide the enterprise-grade deployment of generative and agentic AI solutions.
- Establish FinOps-for-AI practices focused on cost optimization and efficient use of AI resources.
- Embed AI security, governance, and risk management into platform architecture and engineering practices.
- Partner with clients and pursuit teams to shape solutions, architectures, and delivery models for major AI programs.
- Support the development of innovative AI solutions that address complex challenges within the Resources and Utilities sector.
- Travel as required based on business needs and client requirements, with travel potentially ranging from 0% to 100%.
Required Qualifications
- 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 or LLM platforms across major cloud and model providers.
- Minimum of 3 years of experience with LLMOps or MLOps at scale and multi-cloud architecture involving AWS, Azure, and Google.
- Minimum of 5 years of experience leading engineering teams and managing end-to-end solution delivery.
- Minimum of 3 years of experience with AI security, governance, and cost optimization, including FinOps for AI.
- Minimum of 1 year of experience serving utilities clients in electric, gas, or water sectors, or experience in utilities finance, controllership, or regulatory functions.
- Bachelor’s degree or equivalent experience, with a minimum of 12 years of work experience.
- Candidates with an Associate’s degree must have a minimum of 6 years of equivalent work experience.
Preferred Qualifications
- Master’s degree in a relevant field.
- Cloud architecture or AI engineering certifications from AWS, Azure, or Google.
- Demonstrated thought leadership in enterprise AI.
- Published innovation work related to enterprise artificial intelligence.
- Experience developing and implementing enterprise AI strategies across complex organizational environments.
- Strong understanding of emerging generative and agentic AI technologies.
- Experience establishing secure, reliable, and scalable AI engineering practices.
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