Senior AI Architect

GE Vernova

. Define and maintain MLOps, DevOps, and cloud reference architectures across cloud, edge, on-premises, and hybrid environments .

Posted 9/22/2026full-timeGreenville • United StatesSenior💰 $113,200 - $188,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and maintaining MLOps and DevOps architectures across various environments, with a strong focus on AI workload integration and production quality standards. Proven ability to mentor teams and establish technical standards for AI solutions while ensuring compliance with security and performance requirements.

Highest-signal resume keywords

MLOps ArchitectureAI Workload IntegrationCI/CD Pipeline DesignKubernetes ExperienceMentoring AI Engineering Talent

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Solution DeploymentEnterprise Architecture DesignTechnical Standards DefinitionModel Lifecycle ManagementAutomated Testing and Validation

Soft Skills

Strong Communication SkillsMentoring and Development

Tools & Technologies

KubernetesMLflowTerraformAirflowPrometheusGrafanaARC FoundryAMPGE Vernova

Industry Keywords

MLOpsDevOpsAI/ML SystemsCloud ArchitectureData Protection

Tech Stack

Tools & technologies

AirflowCloudCyber SecurityGrafanaKubernetesPrometheusTerraform

About the role

Key responsibilities & impact

Define and maintain MLOps, DevOps, and cloud reference architectures across cloud, edge, on-premises, and hybrid environments
Define enterprise infrastructure and integration patterns for AI workloads
Make architecture decisions based on data sensitivity, latency, compute demand, cost, reliability, and workflow requirements
Design standardized CI/CD and continuous training patterns
Establish automated pipelines for building, testing, validating, securing, packaging, deploying, and rolling back AI solutions
Define production quality gates for software, model performance, data quality, security, and engineering validation
Own model lifecycle standards from development and validation through deployment, monitoring, retraining, retirement, and replacement
Establish model registration, versioning, provenance, approval, and release-history practices
Define observability standards, performance baselines, service-level expectations, alert thresholds, and escalation paths
Establish diagnostic, incident-response, rollback, recovery, and post-incident learning practices
Integrate cybersecurity, identity, access control, secrets management, network, data protection, and audit requirements
Assess technical readiness for transition into sustained GE Vernova operation
Require maintainable code, automated deployment, documentation, monitoring, test coverage, version history, and support ownership before transfer
Review AI subsystem designs for deployability, scalability, reliability, security, maintainability, observability, cost, and supportability
Identify production risks and resolve technical escalations with documented architecture decisions
Mentor AI Architects and engineering teams and define competency expectations and development pathways

Requirements

What you’ll need

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
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, including Kubernetes, MLflow or similar registries, Terraform, Airflow/Kubeflow, Prometheus/Grafana, and enterprise data platforms
Ability to partner with engineering leaders, Digital/IT teams, platform owners, and governance stakeholders
Strong communication skills and ability to explain complex technical concepts to non-technical audiences
Experience with ARC Foundry, AMP, or GE Vernova enterprise AI platforms and integration patterns
Familiarity with agentic AI frameworks such as n8n, LangGraph, and CrewAI
Experience designing multi-agent workflow architectures for engineering applications
Experience mentoring and developing AI engineering talent across distributed teams or matrixed organizations
Comfort with Lean and engineering standard work concepts
Legally authorized to work in the United States

Benefits

Comp & perks

Competitive compensation
Professional development
Relocation assistance
Discretionary annual bonus
Medical, dental, vision, and prescription drug coverage
Health Coach access
Employee Assistance Program
GE Vernova Retirement Savings Plan
Tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions
Fidelity resources and financial planning consultants
Tuition assistance
Adoption assistance
Paid parental leave
Disability benefits
Life insurance
12 paid holidays
Permissive time off