Senior Principal AI Software Engineer

GE Vernova

. Establish a technical vision and oversee and lead AI architecture and platforms .

Posted 10/8/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI architecture, including the integration of LLMs, NLP, and predictive analytics, while leading technical teams and driving AI strategy across business units. Proficient in designing and deploying production GenAI applications and establishing best practices for responsible AI.

Highest-signal resume keywords

AI Architecture LeadershipGenAI Application DevelopmentCloud Platform ExpertiseTechnical MentorshipAI Strategy Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingJava ProgrammingNode.js ProgrammingGo ProgrammingLLM IntegrationVector Database ExperienceAI Orchestration FrameworksContainerization (EKS)Software Development LifecycleSemantic Search Optimization

Soft Skills

Strong Communication SkillsSystematic Problem-SolvingSense of OwnershipDrive

Tools & Technologies

AWSGCPAzureLangChainLlamaIndexPgvectorMilvusPineconeAI Observability ToolsPerformance Monitoring

Industry Keywords

Responsible AIData PrivacyBias MitigationModel MonitoringCross-Organizational CollaborationTechnical Strategy DevelopmentTechnology Roadmap Planning

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaJavaScriptNode.jsPythonGo

About the role

Key responsibilities & impact

Establish a technical vision and oversee and lead AI architecture and platforms
Identify reusable foundational services to accelerate AI application development
Conceive, evaluate, and design Industrial AI Service offerings using AI expertise, competitive intelligence, and customer knowledge
Guide strategic AI design choices and critical design areas early in development
Drive new ways of thinking across business-unit groups to improve quality, engineering productivity, and responsiveness
Integrate LLMs, agent workflows, NLP, computer vision, RAG, and predictive analytics into secure, high-availability, cost-efficient AI solutions
Select appropriate internal or external technologies, incorporate research, and create reusable designs across teams
Partner with product, data science, engineering leaders, business executives, and CIOs
Mentor teams and raise standards in AI development, testing, security, and documentation
Influence AI strategy, direction, and policy
Lead cross-team initiatives and large-scale AI programs across business segments
Design agent workflows and RAG architecture; develop approaches for custom LLMs using domain data
Represent GE Vernova as a subject-matter expert with partners, vendors, and customers
Develop new practices based on AI trends
Review business requirements and clarify trade-offs
Establish AI cost management, monitoring, and security standards

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Computer Engineering, or related technical field
Extensively demonstrated programming experience in a relevant language using modern languages (Python preferred, Java, Node.js or Go)
Proven experience designing and deploying production GenAI applications that use large language models at scale
Experience with vector databases (e.g. pgvector, Milvus, Pinecone) and advanced embedding model optimisation in production applications
Expertise with multiple LLM providers, advanced AI orchestration frameworks (LangChain, LlamaIndex, custom frameworks), Eval frameworks and creating custom agents
Extensive experience leading technical teams, creating technical strategy, and providing technical mentorship
Expert-level experience with cloud platforms (i.e AWS, GCP, or Azure) and advanced containerisation (EKS)
Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and model monitoring
Systematic problem-solving approach, strong communication skills, a sense of ownership and drive
Preferred: Extensively demonstrated full software development lifecycle experience, including architecture design, technical leadership, and large-scale system optimization
Preferred: Advanced understanding of enterprise RAG architectures, semantic search optimisation, and conversation memory management at massive scale
Preferred: Experience with multi-modal AI integration, model fine-tuning, and deployment of custom AI models
Preferred: Experience with AI observability tools, cost optimisation strategies for different AI application types, and performance monitoring at enterprise scale
Preferred: Experience with cross-organisational collaboration and communicating technology strategy across a variety of stakeholders
Preferred: Experience with technical strategy development and technology roadmap planning

Benefits

Comp & perks

Relocation assistance provided
Growth, autonomy, and collaboration across research and product