Job Description
Summary 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. 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.

Key Responsibilities
Define the MLOps, DevOps, and Cloud Architecture for Wind Engineering AI 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. Establish Production-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. 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. 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. Embed Security, Governance, and Auditability by Design 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. Require maintainable code, automated deployment, operating documentation, monitoring, test coverage, version history, and clearly assigned support ownership before transfer. Identify production risks early,



particularly where research prototypes, local infrastructure, manual processes, or undocumented dependencies could prevent scale. 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. Required Qualifications Bachelor's degree in Engineering, Computer Science, Applied Mathematics, Data Science, or a related technical field; 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. 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. Benefits, Salary and Other Information GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. Relocation Assistance Provided: Yes For candidates applying to a U.
Available benefits include medical, dental, vision, and prescription drug coverage; 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. xqbhyrx Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.

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