Job Description Summary

The Senior Staff AI Architect will lead the integrated execution of complex, cross-functional AI programs within GE Vernova Power. The role combines strong AI technical judgment with senior program leadership to translate strategic and architectural direction into executable plans and to drive decisions, dependencies, risks, and partner commitments to closure.

The individual will work across AI Delivery, AI Foundry, Product Management, Infrastructure, AI Program Management, business and engineering teams, and strategic technology partners. This is not limited to the technical accountability of an architect — the role will work alongside data scientists and portfolio leaders, integrate their contributions into one coherent program plan, and establish the operating mechanisms needed to deliver predictable outcomes.

The initial primary assignment will be a strategic Foundation Model development effort, with the opportunity to establish reusable execution practices for other strategic AI programs, AI observability and operational readiness, and partner-supported initiatives.

Job Description

Roles and Responsibilities

• Lead the integrated execution of complex, cross-functional AI programs from definition through technical validation, deployment readiness, operationalization, and adoption.
• Translate strategic and architectural direction into an executable program plan with measurable outcomes, milestones, dependencies, decision points, technical gates, and accountable owners.
• Integrate work across AI Architecture, AI Foundry, Product Management, Infrastructure, AI Program Management, business teams, engineering teams, and strategic partners into one coherent plan.
• Own the integrated program plan, execution rhythm, dependency closure, risk management, decision governance, partner coordination, and recovery planning.
• Apply strong AI technical judgment to challenge assumptions, sequencing, estimates, validation approaches, infrastructure requirements, integration dependencies, and operational-readiness evidence.
• Facilitate complex technical decisions while preserving the accountability of designated architecture, data-science, platform, security, and engineering authorities.
• Drive decisions, dependencies, risks, commitments, and outcomes through teams and partners without relying on direct reporting authority.
• Identify execution gaps early and require recovery plans, scope decisions, resource actions, or leadership escalations.
• Serve as a senior execution counterpart for strategic technology partners, integrating their work into the program while retaining internal ownership of technical direction and acceptance.
• Maintain a clear, evidence-based leadership view of program outcomes, milestones, decisions, risks, dependencies, and required actions.
• Establish repeatable mechanisms for integrated planning, technical-gate readiness, decision closure, partner delivery, observability, operational readiness, and leadership reporting — and extend these as reusable practices across strategic AI initiatives.
• Drive reuse and adoption of approved AI platforms, reusable services, architectural patterns, and validation approaches across strategic AI programs.
• Comfortable operating with ambiguity, incomplete information, distributed accountability, and changing program conditions; outcome-oriented and pragmatic, balancing technical rigor, delivery urgency, enterprise standards, and long-term reusability.
• Able to move fluidly between technical depth, integrated program leadership, partner management, and executive communication.

Required Qualifications

• • A Minimum of Bachelor’s degree in computer science, Engineering, Data Science, Information Technology, or a related technical discipline (or equivalent professional experience), with significant experience leading complex, cross-functional technology programs involving AI, machine learning, data platforms, cloud platforms, or enterprise applications.
  • Strong working knowledge of AI and machine-learning delivery lifecycles — data readiness, experimentation, model development, evaluation, integration, deployment, monitoring, and operationalization — combined with experience spanning both AI/software architecture and coordinated delivery working along with data science, software engineering, infrastructure, product management, program management, business teams, and external partners.
  • Strong executive communication skills, including the ability to translate complex technical and execution issues into clear options, recommendations, decisions, and business implications, and to influence senior technical contributors, leaders, and strategic partners in a complex matrixed organization.
  • Experience establishing program operating rhythms, integrated plans, decision mechanisms, technical-readiness reviews, risk-management practices, and executive reporting that deliver predictable outcomes.

Desired Characteristics

• Experience leading enterprise or industrial AI programs — time-series analytics, predictive analytics, generative AI, agentic AI, or data-platform initiatives — ideally in energy, industrial, manufacturing, asset-intensive, or regulated environments.
• Hands-on familiarity with cloud and hybrid platforms, including AWS and Azure as well as on-premises AI and data environments, with practical knowledge of AWS-based AI/ML and data services relevant to this program.
• Understanding of modern AI engineering practices, including MLOps, LLMOps, model evaluation, observability, responsible AI, data governance, platform engineering, and production operations.
• Experience working with strategic technology partners, system integrators, consulting organizations, or externally supported engineering teams.
• Strong systems-thinking skills, with the ability to connect business outcomes, architecture, data, infrastructure, models, applications, operations, and organizational dependencies.

Additional Information