Senior AI​/ML Applications Architect

Sr AI/ML Applications Architect – GE Vernova

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. Join us to electrify and decarbonize the world.

We are seeking a highly skilled Sr AI/ML 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.

• 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 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‑first, 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.
• Ensure the design and development of AI/ML solutions and project deliveries adhere to the defined framework and are scalable, high‑performant, maintainable, accurate and reliable.
• Drive technical decision‑making for AI/ML solutions and projects including infrastructure requirements and deployment strategies for both edge computing and cloud‑based solutions.
• 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 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.
• Translate research insights and emerging technologies into practical solutions that can be integrated into our product lines.
• Foster a culture of innovation and learning within the team by encouraging experimentation with new technologies and knowledge sharing of industry developments.
• Lead end‑to‑end project delivery from ideation through deployment, ensuring projects meet technical requirements, timelines and business objectives.
• Manage and mentor a small team of AI/ML engineers, data scientists and data engineers, providing technical guidance and career development support.
• Coordinate cross‑functional project teams, facilitating collaboration between engineering, product, operations and business stakeholders.
• Collaborate on resource planning and risk mitigation for complex AI/ML projects.
• Ensure AI/ML solutions meet industry standards, regulatory requirements and cybersecurity protocols for critical energy infrastructure.

• PhD or Master’s with a minimum of 5 years equivalent professional experience, in Computer Science, Electrical Engineering, Data Science or related technical field.
• Minimum of 10 years of hands‑on experience in AI/ML development with 8+ years in architectural roles.

• Proven expertise in machine learning frameworks (Tensor Flow, PyTorch, Scikit‑learn, etc.) and generative AI technologies (LLMs, SLMs, diffusion models, GANs).
• Proven…