Senior AI Architect

GE Vernova • Greenville, NC

9h

About The Position

The 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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• Define and maintain reference 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.
• 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.
• 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.
• 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 for production AI 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 for production AI systems.
• Integrate cybersecurity, identity, access control, secrets management, 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.
• 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 assigned support ownership before transfer.
• Ensure reusable components and lessons learned are incorporated into enterprise standards and reference architectures.
• Review subsystem AI designs for deployability, scalability, reliability, security, maintainability, observability, cost, and supportability.
• Identify production risks early, particularly where research prototypes, local infrastructure, manual processes, or undocumented dependencies could prevent scale.
• 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 and maintaining production AI solutions.

Benefits

• medical, dental, vision, and prescription drug coverage
• access to Health Coach from GE Vernova, a 24/7 nurse-based resource
• access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services
• 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
• tuition assistance
• adoption assistance
• paid parental leave
• disability benefits
• life insurance
• 12 paid holidays
• permissive time off

What This Job Offers

• Job Type: Full-time
• Career Level: Senior