Job Description SummaryThe Senior AI Architect is an enterprise technical authority responsible for defining scalable AI architectures and guiding consistent implementation across Wind Engineering. This role translates AI strategy into durable technical patterns, standards, and reference architectures that enable Wind Engineering to build AI solutions that are reusable, secure, maintainable, and aligned to enterprise platforms.
Senior AI Architects mentor and develop Embedded AI Architects and provide technical oversight across the AI portfolio, ensuring that solutions built within subsystems conform to enterprise standards and can be reused across Wind Engineering. This role is also responsible for updating engineering standard work and quality management systems to reflect AI-forward advancements.
Reporting to the Director – AI Strategy & Transformation, the ideal candidate is a technically deep, architecturally minded leader who combines hands-on AI engineering expertise with the ability to define and enforce standards, influence without authority, and operate at the intersection of AI, engineering, and process workflows.
Job DescriptionKey Responsibilities
1. Define and Maintain Enterprise AI Architectures and Reusable Solution Patterns
- Design, publish, and maintain enterprise AI reference architectures that provide Wind Engineering with proven, scalable patterns for common AI solution types (e.g., agentic workflows, RAG-based knowledge systems, time-series forecasting, computer vision).
- Develop and maintain a library of reusable AI solution patterns, components, and design templates that Embedded AI Architects can leverage to accelerate delivery and ensure consistency.
- Establish architecture standards that address data ingestion, deployment, monitoring, versioning, and lifecycle management for AI solutions across Wind Engineering.
- Ensure reference architectures are aligned with GE Vernova enterprise platforms, ARC Foundry capabilities, Digital/IT infrastructure, and approved tooling.
- Regularly review and update architectures as AI technology, enterprise platforms, and Wind Engineering use cases evolve.
2. Create and Maintain AI Design Practices
- Define Wind Engineering's AI solution design practices, including design review processes, architecture decision records, and technical documentation standards.
- Establish standards for how AI solutions are scoped, designed, validated, deployed, and maintained across the Wind Engineering portfolio.
- Develop design principles that prioritize scalability, reusability and engineering workflow integration from the earliest stages of solution design.
- Create and maintain AI architecture review checkpoints within the portfolio stage-gate process, ensuring technical quality is evaluated at key maturity transitions.
3. Update Engineering Standard Work and Quality Management Systems with AI Advancements
- Identify opportunities to embed AI-forward practices into existing engineering standard work, design processes, and quality management system (QMS) documentation.
- Lead the integration of AI-assisted workflows into Wind Engineering standard processes, including AI-assisted FMEA updates, design review workflows, and validation procedures.
- Ensure that AI tools and capabilities introduced into engineering workflows are supported by updated standard work documentation, training materials, and process guidance.
- Work with engineering leaders and quality teams to define how AI-generated outputs are reviewed, validated, and incorporated into engineering records and decision-making.
- Establish the technical baseline for how AI contributes to engineering quality.
4. Partner with Digital and IT on Platforms, MLOps, and Integration Standards
- Serve as the primary technical liaison between Wind Engineering AI programs and Digital/IT, ARC Foundry, and enterprise platform teams.
- Define MLOps and LLMOps requirements for Wind Engineering, including model training pipelines, deployment automation, performance monitoring, drift detection, and retraining governance.
- Evaluate platform options, integration approaches, and tooling choices that enable scalable, maintainable AI deployment across Wind Engineering engineering environments.
- Ensure AI solutions built on enterprise platforms (AMP, AWS, Azure, GE Vernova digital infrastructure) leverage approved integration patterns and do not create technical debt or unsupported dependencies.
- Define and maintain Wind Engineering's AI toolchain standards, covering development environments, model repositories, data pipelines, orchestration frameworks, and deployment targets.
5. Review and Guide Subsystem AI Designs for Scalability and Reuse
- Conduct architecture reviews for AI solutions being developed within subsystems and functions, evaluating alignment to enterprise standards, scalability, security, maintainability, and reuse potential.
- Identify opportunities where solutions designed for one subsystem could be abstracted and reused across multiple Wind Engineering domains, and drive that reuse proactively.
- Provide technical guidance to Embedded AI Architects at critical design, integration, and deployment decision points.
- Maintain visibility of the full Wind Engineering AI solution portfolio from a technical architecture perspective, identifying patterns, gaps, redundancies, and cross-subsystem dependencies.
- Ensure solutions are not built in architectural isolation, and that local technical decisions do not foreclose enterprise-scale reuse or create integration complexity.
6. Mentor and Develop Embedded AI Architects
- Provide consistent technical mentorship, coaching, and development support to the Embedded AI Architect community across Wind Engineering subsystems.
- Define technical competency expectations for Embedded AI Architects and create structured development pathways aligned to those expectations.
- Facilitate regular technical forums, design reviews, and knowledge-sharing sessions across the Embedded AI Architect community to accelerate cross-pollination of ideas and proven approaches.
- Support Embedded AI Architects in navigating complex technical decisions, vendor evaluations, and architecture tradeoffs within their subsystems.
- Ensure Embedded AI Architects have access to the reference materials, tools, standards, and mentorship needed to deliver high-quality AI solutions within their domain.
7. Serve as Technical Escalation Point for Complex or High-Risk AI Initiatives
- Act as the senior technical authority for AI solutions that are high-risk, architecturally novel, cross-subsystem in scope, or under consideration for enterprise-scale deployment.
- Provide technical input to governance reviews, build vs. buy vs. ARC Foundry decisions, and vendor evaluations for AI platforms and tools.
- Support the AI Governance Leader in assessing the technical dimensions of model risk, data integrity, cybersecurity posture, and auditability for AI solutions under governance review.
- Evaluate the technical feasibility, scalability, and sustainability of proposed AI approaches at early stages to prevent costly architectural missteps.
- Maintain technical leadership credibility through direct engagement with complex AI engineering problems across Wind Engineering.
Required Qualifications:
- Bachelor's degree in Engineering, Computer Science, Applied Mathematics, Data Science, or a related technical field; advanced degree strongly preferred.
- Significant hands-on experience designing, building, and deploying AI or machine learning solutions in complex technical environments, with demonstrated progression to enterprise-level architecture responsibilities.
- Deep technical knowledge across AI/ML domains relevant to industrial engineering, including supervised learning, deep learning, time-series analysis, generative AI, agentic workflows, and physics-informed modeling.
- Experience defining technical standards, reference architectures, and design practices across a portfolio of AI solutions.
- Familiarity with MLOps platforms, cloud AI infrastructure (AWS, Azure, or GCP), model serving frameworks, and enterprise data platforms.
Desired Characteristics:
- Ability to partner effectively with engineering leaders, Digital/IT teams, platform owners, and governance stakeholders.
- Strong communication skills, with the ability to explain complex technical concepts to non-technical audiences and translate architecture standards into practical guidance.
- Practical experience with AI in engineering contexts, including design automation, simulation acceleration, FMEA support, predictive maintenance, anomaly detection, or AI-assisted validation workflows.
- Experience with ARC Foundry, AMP, or GE Vernova enterprise AI platforms and integration patterns.
- Familiarity with agentic AI frameworks (e.g., n8n, LangGraph, CrewAI) and experience designing multi-agent workflow architectures for engineering applications.
- Knowledge of QMS integration requirements and experience embedding AI outputs into quality management and engineering documentation systems.
- Experience mentoring and developing AI engineering talent across distributed teams or matrixed organizations.
- Comfortable with Lean and engineering standard work concepts, with the ability to apply AI architecture thinking to process improvement and waste elimination.
- Strong ownership mindset, equally comfortable driving technical strategy at the enterprise level and reviewing detailed subsystem-level design decisions.
- Technical escalations are resolved promptly, with documented architecture decisions and rationale available for future reference.
For candidates applying to a U.S. based position, the pay range for this position is between $113,200.00 and $188,800.00. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.
Bonus eligibility: discretionary annual bonus.
This posting is expected to remain open for at least seven days after it was posted on July 09, 2026.
Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life ins
