Senior AI​/ML Applications Architect

Overview

GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy, while helping our customers power economies and deliver the electricity that is vital to health, safety, security, and improved quality of life. Are you excited at the opportunity to electrify and decarbonize the world? We are seeking an experienced and highly skilled Applications Architect to lead the design, development and deployment of advanced machine learning (ML) and generative AI solutions.

The role combines deep technical expertise in AI/ML architecture with leadership responsibilities, requiring someone who can drive innovation from concept to production while managing high‑performing project teams. This role will also involve developing Proof of Concepts (PoCs) and ensuring the deployment of models on the edge or cloud‑based systems. This position will collaborate closely with Grid Automation (GA) product lines, R&D teams, product management, and other GA functions to drive efficiency and innovation.

• Design and architect scalable AI/ML solutions, including generative AI applications, tailored to grid automation and digitalization technologies as well as business efficiency.
• Ensure optimal performance of these solutions across edge and cloud deployment environments.
• Establish architectural standards, best practices and technical guidelines for AI/ML development across the CTO organization in collaboration with GEV AI/ML partners.
• Build a strong technical foundation with architecture built on modular/microservices, cloud/edge, API 1st, privacy by design philosophies; infrastructure concepts of containerization, orchestration, auto‑scale capabilities (compute, storage, network) and infra‑as‑code; development concepts of automation (CI/CD, data and MLOps pipelines), code assist and sandboxes for collaboration + experimentation.
• Design and deploy on GE Grid Node/edge platforms, using container and microservices principles and best practices.
• Develop and implement strategies for optimizing performance of models in production.
• Collaborate with cross‑functional teams to integrate AI/ML capabilities into existing platforms and develop new intelligent business efficiency and product line solutions.
• Stay current with state‑of‑the‑art developments in AI/ML, generative AI and energy systems technology through continuous monitoring of research and industry trends.
• Evaluate and recommend emerging technologies and methodologies (AIML tools, platforms, vendor solutions) for their potential application to grid automation challenges and business opportunities; design, execute and demo proof‑of‑concepts (PoCs) to validate new AI/ML approaches and assess their feasibility for energy system applications.

• Minimum of a Bachelor’s degree in Computer Science, Electrical Engineering, Data Science or related technical field.
• Minimum of 7 years of hands‑on experience within software engineering, AI/ML development and/or architectural roles.

• Proven expertise in machine learning frameworks (Tensor Flow, PyTorch, Scikit‑learn, etc.) and generative AI technologies (LLMs, SLMs, diffusion models, GANs).
• Proven experience in applying AI/ML frameworks/workflows, AI/MLOps and CI/CD using cloud‑native and on‑prem development and deployment in operational technology/industrial automation environments.
• Experience developing and implementing ML models using cloud MLOps pipelines such as AWS Sage Maker, Azure ML, Google Vertex
  
  AI, Dataiku Cloud or equivalent.
• Hands‑on professional experience in developing and testing AI/ML algorithms and demonstrated professional experience with grid/physics models in power system simulation tools, MATLAB/PSCAD; as well as power system analysis SW such as PSS/E, Digsilent or equivalent.
• Experience with Dev Ops, data pipelines, Azure ML registry, deployment methods (Docker, K8s, etc.).
• Proven experience designing solutions that include the full AI/ML project lifecycle: data acquisition (real‑time/streaming, batch and response/request), data quality assurance + engineering, model selection and evaluation, tuning, testing, deployment, maintenance…