Job Description SummaryThe Senior AI Architect, MLOps / DevOps / Cloud Engineering is an enterprise technical authority responsible for defining how AI solutions are productionized, deployed, operated, monitored, secured, and continuously improved across Wind Engineering.
Building on the broader Senior AI Architect mandate, this role provides specialized leadership in MLOps, DevOps, cloud and hybrid infrastructure, distributed AI systems, CI/CD, observability, model lifecycle management, and production reliability. The role establishes the architectures, engineering practices, reusable components, and operational standards required to move AI models and workflows from experimentation into reliable, maintainable, and scalable engineering products.
This person partners closely with Digital/IT, ARC Foundry, enterprise platform teams, AI engineers, data engineers, and Embedded AI Architects to ensure solutions use approved infrastructure and integration patterns, are designed for sustainable operation, and can transition into GE Vernova ownership without dependence on fragile code, undocumented environments, or external support.
Job DescriptionKey Responsibilities:
1. Define theMLOps, DevOps, and Cloud Architecture for Wind Engineering AI
- Define andmaintainreference architectures for deploying and operating AI solutions across cloud, edge, on-premises, and hybrid environments.
- Define how AI workloads use enterprise environments, including approved cloud services, container platforms, model repositories, data platforms, APIs, engineering applications, and authentication services.
- Make architecture decisions across cloud, edge, and on-premises execution based on data sensitivity, latency, compute demand, cost, reliability, and engineering workflow requirements.
2.EstablishProduction-Grade AI Delivery Pipelines
- Design standardized CI/CD and continuous training patterns for AI-enabled engineering applications.
- Establish automated pipelines for code build, testing, model validation, security checks, packaging, deployment, and rollback.
- Define quality gates that prevent models or AI services from progressing into production unless they meet documented software, model-performance, data-quality, security, and engineering-validation criteria.
3. Own Model Lifecycle and Production Operations Standards
- Define the operating model for AI models from development and validation through deployment, monitoring, retraining, retirement, and replacement.
- Establish model-registration and versioning practices that preserve provenance, approval evidence, performance baselines, applicability limits, dependencies, and release history.
4. Build AI Observability, Reliability, and Drift-Management Practices
- Define observability standards for AI applications, model services, pipelines, APIs, workflows, and supporting infrastructure.
- Define performance baselines, service-level expectations, alert thresholds, and escalation paths forproductionAI solutions.
- Establish diagnostic practices that distinguish model issues from data, application, infrastructure, integration, or workflow failures.
- Define incident-response, rollback, recovery, and post-incident learning practices forproductionAI systems.
5. Embed Security, Governance, and Auditability by Design
- Integrate cybersecurity, identity, access control,secretsmanagement, network, data-protection, and audit requirements into AI platform and deployment architectures.
- Partner with AI Governance, cybersecurity, Digital/IT, and platform teams to translate policies into enforceable technical controls.
6. Lead Technical Transfer and Sustainable GE Vernova Ownership
- Assess whether AI applications are technically ready to transition from external partners, research teams, or pilot environments into sustained GE Vernova operation.
- Require maintainable code, automated deployment, operating documentation, monitoring, test coverage, version history, and clearly assignedsupportownership before transfer.
- Ensure reusable components and lessons learned are incorporated into enterprise standards and reference architectures.
7. Provide Portfolio Architecture Review and Technical Escalation
- Review subsystem AI designs fordeployability, scalability, reliability, security, maintainability, observability, cost, and supportability.
- Identifyproduction risks early, particularly where research prototypes, local infrastructure, manual processes, or undocumented dependencies could prevent scale.
8. Mentor Architects and Raise Production Engineering Capability
- Mentor AI Architects and AI engineering teams in cloud architecture, DevOps,MLOps, observability, testing, security, and production-readiness practices.
- Define competency expectations and practical development pathways for engineers responsible for building andmaintainingproductionAI solutions.
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.
- Experience defining technical standards, reference architectures, and design practices across a portfolio of AI solutions.
- Familiarity with AI/ML systems in production (e.g., Kubernetes, MLflow or similar registries, Terraform, Airflow/Kubeflow, Prometheus/Grafana) 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.
- 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.
- 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 candidate27s 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 September 22, 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 insurance, 12 paid holidays, and permissive time off.
