Sr Software Architect - AI/ML

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.
• 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.
• 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…