Job Description Summary

GE Vernova’s Grid Software team is at the forefront of the energy transition, delivering the software solutions that power the world’s most critical power grids. We are seeking a Senior Software Architect and AI Services Expert to lead our technological evolution.
This is not a traditional architecture role. We are looking for a hands-on technical leader who will bridge the gap between high-level architectural strategy and the next generation of AI-assisted software delivery. You will define our AI-enabled Services methodology, ensuring that our teams move beyond ad-hoc experimentation to a disciplined, production-ready, and scalable development workflow. You will be instrumental in driving productivity, optimizing delivery cycles, and generating measurable value for our utility customers.

Job Description

Core Mission
You will serve as the solution owner for all AI-assisted Services implementations within the Grid Software portfolio. Your goal is to instill a culture of "AI-assisted discipline"—where AI is a force multiplier for high-quality, secure, and maintainable code, rather than a shortcut for speed at the expense of integrity. You will be required to provide hands on guidance and accelerate the AI adoption into various activities of Grid SW services across the global teams as at any point in time there will be multiple projects running in parallel which will require your intervention 

Key responsibilities

Define and Maintain Enterprise AI Architectures and Reusable Solution Patterns

• Design, publish, and maintain enterprise AI reference architectures that provide Grid Software with proven, scalable patterns for common AI solution types (e.g., agentic workflows, RAG-based knowledge systems, time-series forecasting, computer vision).
• Develop and maintain a library of reusable AI solution patterns, components, and design templates that Embedded AI Architects can leverage to accelerate delivery and ensure consistency.
• Establish architecture standards that address data ingestion, deployment, monitoring, versioning, and lifecycle management for AI solutions across Grid Software Services.
• Ensure reference architectures are aligned with GE Vernova enterprise platforms, ARC Foundry capabilities, Digital/IT infrastructure, and approved tooling.
• Regularly review and update architectures as AI technology, enterprise platforms, and Grid Software Services use cases evolve.

2. Create and Maintain AI Design Practices

• Define Grid Software Services’ AI solution design practices, including design review processes, architecture decision records, and technical documentation standards.
• Establish standards for how AI solutions are scoped, designed, validated, deployed, and maintained across the Grid Software Services portfolio.
• Develop design principles that prioritize scalability, reusability and Services workflow integration from the earliest stages of solution design.
• Create and maintain AI architecture review checkpoints within the portfolio stage-gate process, ensuring technical quality is evaluated at key maturity transitions.

3. Update Services Standard Work and Quality Management Systems with AI Advancements

• Identify opportunities to embed AI-forward practices into existing Services standard work, design processes, and quality management system (QMS) documentation.
• Lead the integration of AI-assisted workflows into Grid Software Services standard processes, including AI-assisted updates, design review workflows, and validation procedures.
• Ensure that AI tools and capabilities introduced into Services workflows are supported by updated standard work documentation, training materials, and process guidance.
• Work with Services leaders and quality teams to define how AI-generated outputs are reviewed, validated, and incorporated into Services records and decision-making.
• Establish the technical baseline for how AI contributes to Services quality.

4. Partner with Digital and IT on Platforms, MLOps, and Integration Standards

• Serve as the primary technical liaison between Grid Software Services AI programs and Digital/IT, ARC Foundry, and enterprise platform teams.
• Define MLOps and LLMOps requirements for Grid Software Services, including model training pipelines, deployment automation, performance monitoring, drift detection, and retraining governance.
• Evaluate platform options, integration approaches, and tooling choices that enable scalable, maintainable AI deployment across Grid Software Services Services environments.
• Ensure AI solutions built on enterprise platforms (AMP, AWS, Azure, GE Vernova digital infrastructure) leverage approved integration patterns and do not create technical debt or unsupported dependencies.
• Define and maintain Grid Software Services's AI toolchain standards, covering development environments, model repositories, data pipelines, orchestration frameworks, and deployment targets.

5. Review and Guide AI Designs for Scalability and Reuse

• Conduct architecture reviews for AI solutions being developed within subsystems and functions, evaluating alignment to enterprise standards, scalability, security, maintainability, and reuse potential.
• Identify opportunities where solutions designed for one subsystem could be abstracted and reused across multiple Grid Software Services domains, and drive that reuse proactively.
• Provide technical guidance to project  AI solution teams at critical design, integration, and deployment decision points.
• Maintain visibility of the full Grid Software Services AI solution portfolio from a technical architecture perspective, identifying patterns, gaps, redundancies, and cross-subsystem dependencies.
• Ensure solutions are not built in architectural isolation, and that local technical decisions do not foreclose enterprise-scale reuse or create integration complexity.

6. Mentor and Develop AI Architects

• Provide consistent technical mentorship, coaching, and development support to the AI Architect community across Grid Software Services.
• Define technical competency expectations for AI practitioners and create structured development pathways aligned to those expectations.
• Facilitate regular technical forums, design reviews, and knowledge-sharing sessions across the AI practitioner community to accelerate cross-pollination of ideas and proven approaches.
• Support AI practitioners in navigating complex technical decisions, vendor evaluations, and architecture tradeoffs within their subsystems.
• Ensure AI practitioners have access to the reference materials, tools, standards, and mentorship needed to deliver high-quality AI solutions within their domain.

7. Serve as Owner for Complex AI Initiatives

• Act as the senior technical authority for AI solutions that are high-risk, architecturally novel, cross-subsystem in scope, or under consideration for enterprise-scale deployment.
• Provide technical input to governance reviews, build vs. buy vs. ARC Foundry decisions, and vendor evaluations for AI platforms and tools.
• Support the AI Governance Leader in assessing the technical dimensions of model risk, data integrity, cybersecurity posture, and auditability for AI solutions under governance review.
• Evaluate the technical feasibility, scalability, and sustainability of proposed AI approaches at early stages to prevent costly architectural missteps.
• Maintain technical leadership credibility through direct engagement with complex AI Services problems across Grid Software Services.

Required Qualifications:

• 15+ years of strong hands Software development experience, technical architecture, and working with large scale enterprise systems.
• Bachelor’s degree in services, Computer Science, Applied Mathematics, Data Science, or a related technical field; advanced degree strongly preferred.
• Proven expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.) and generative AI technologies (LLMs, SLMs, diffusion models, GANs).
• 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.
• Deep technical knowledge across AI/ML domains relevant to industrial Services, including supervised learning, deep learning, time-series analysis, generative AI, agentic workflows, and physics-informed modeling.
• Experience defining technical standards, reference architectures, and design practices across a portfolio of AI solutions.
• Familiarity with MLOps platforms, cloud AI infrastructure (AWS, Azure, or GCP), model serving frameworks, and enterprise data platforms.
• Strong ability to focus on business outcomes by AI augmentation into existing processe and Strong trouble shooting skills

Desired Characteristics:

• Ability to partner effectively with Services 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.
• Practical experience with AI in Services contexts, including design automation, simulation acceleration, predictive maintenance, anomaly detection, or AI-assisted validation workflows.
• Familiarity with agentic AI frameworks (e.g., n8n, LangGraph, CrewAI) and experience designing multi-agent workflow architectures for Services applications.
• Experience mentoring and developing AI Services talent across distributed teams or matrixed organizations.
• Comfortable with Lean and Services 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.
• Good communication skills and prior experience of working with globally distributed teams

Why Join GE Vernova?

At GE Vernova, your work has real-world consequences. You will be building tools that help utilities balance the grid, integrate renewable energy, and ensure power reliability for millions. This role offers the rare opportunity to redefine how an entire industrial Services organization delivers software, setting the global standard for AI-assisted energy innovation.

Additional Information