Senior AI Architect – Generative AI

GE Vernova

. Define reference architectures for foundation-model applications, RAG, tool-using assistants, agentic workflows, document intelligence, and multimodal engineering use cases .

Posted 11/5/2026full-timeUnited StatesSenior💰 $113,200 - $188,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and deploying generative AI systems, with a strong focus on model evaluation, integration with enterprise APIs, and adherence to security standards. Proven ability to mentor teams and communicate complex architectural decisions effectively to diverse stakeholders.

Highest-signal resume keywords

Generative AI ArchitectureModel Evaluation FrameworksEnterprise API IntegrationGenAI Security Risk ManagementMentoring Technical Teams

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Foundation ModelsPrompt/Context EngineeringTool UseModel EvaluationOrchestration FrameworksTest Set DesignVector SearchHybrid SearchDocument ParsingSynthetic Data Generation

Soft Skills

CommunicationMentoringDecision-Making

Tools & Technologies

Azure OpenAIAmazon BedrockGoogle Vertex AILangGraphSemantic KernelN8n

Industry Keywords

RAGAgentic SystemsResponsible AICybersecurity Threat ModelingRegulated Knowledge Workflows

Tech Stack

Tools & technologies

AzureCyber Security

About the role

Key responsibilities & impact

Define reference architectures for foundation-model applications, RAG, tool-using assistants, agentic workflows, document intelligence, and multimodal engineering use cases
Establish approved patterns for model gateways, prompt and context management, retrieval, memory, tool invocation, orchestration, evaluation, logging, and user experience
Define model selection criteria based on quality, security, latency, cost, controllability, and supportability
Ensure solutions align with approved GE Vernova platforms, identity, data, integration, and deployment standards
Define when deterministic automation, a single assistant, or an agentic architecture is appropriate
Establish patterns for planning, tool use, state, memory, handoffs, retries, approvals, termination, and exception handling
Integrate agents with engineering applications, APIs, workflow engines, data systems, simulation tools, and enterprise services using least-privilege access
Define bounded actions, approval gates, transaction limits, and recoverable failure behavior
Define evaluation frameworks covering task success, groundedness, retrieval quality, factuality, completeness, instruction adherence, safety, latency, cost, and user value
Create representative test sets from actual engineering tasks, including difficult, ambiguous, adversarial, and out-of-scope cases
Establish automated, model-based, deterministic, and expert human evaluation methods
Require release gates, regression testing, red-team evidence, and post-deployment monitoring before broad adoption
Design GenAI experiences integrated with engineering decision points, standard work, document flows, and existing applications
Define patterns for human review, structured output, source evidence, approvals, write-back, and exception handling
Ensure generated content entering engineering records is identifiable, reviewable, and appropriately validated
Partner with process owners to measure cycle-time, quality, adoption, and rework impacts
Mentor Embedded AI Architects, AI engineers, data scientists, and product teams
Lead architecture reviews, evaluation clinics, red-team exercises, technical demonstrations, and reusable-pattern workshops
Define competency expectations across RAG, agents, evaluation, security, prompt/context engineering, and workflow integration
Maintain a pragmatic view of emerging models and frameworks, adopting capabilities based on measured engineering value

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, Applied Mathematics, Data Science, or a related technical field; advanced degree strongly preferred
Significant hands-on experience architecting and deploying production generative AI, RAG, conversational, or agentic systems
Knowledge of foundation models, embeddings, retrieval, reranking, prompt/context engineering, structured generation, tool use, and model evaluation
Experience with orchestration and application frameworks such as LangGraph, Semantic Kernel, n8n, or equivalent
Experience integrating AI applications with enterprise APIs, identity, data platforms, document repositories, workflow systems, and observability services
Experience designing test sets and evaluation pipelines for quality, groundedness, safety, regressions, latency, and cost
Understanding of GenAI security risks, including prompt injection, data leakage, insecure tool use, and untrusted content
Ability to define enterprise standards, document decisions, mentor teams, and communicate architecture trade-offs to technical and non-technical stakeholders
Experience with engineering, scientific, manufacturing, service, or regulated knowledge workflows
Experience with Azure OpenAI, Amazon Bedrock, Google Vertex AI, or comparable enterprise model platforms and model gateways
Experience with vector and hybrid search, knowledge graphs, document parsing, multimodal models, fine-tuning, adapters, or synthetic-data generation
Experience implementing agent evaluation, runtime tracing, policy enforcement, and approval-based action execution
Familiarity with Responsible AI frameworks, model risk management, cybersecurity threat modeling, and audit evidence for AI systems
Experience evaluating commercial GenAI platforms and balancing delivery speed against control, portability, ownership, and total cost
Legally authorized to work in the United States
Successful completion of a drug screen, as applicable

Benefits

Comp & perks

Professional development
Challenging careers
Competitive compensation
Discretionary annual bonus
Medical, dental, vision, and prescription drug coverage
24/7 Health Coach nurse-based resource
Employee Assistance Program with 24/7 confidential assessment, counseling and referral services
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
Relocation assistance