Sr AI/ML Applications Architect

Overview

Lead design, development, and deployment of AI/ML solutions for grid automation.

Key Responsibilities

• ai architecture
• model deployment
• edge computing
• team leadership
• project delivery
• tech evaluation

Tasks

-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. -Design and deploy on GE GridNode/edge platforms, using container and microservices principles and best practices. Develop and implement strategies for optimizing performance of models in production. -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. -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. -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. -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. -Drive technical decision-making for AI/ML solutions and projects including infrastructure requirements and deployment strategies for both edge computing and cloud-based solutions. -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. -Collaborate with cross-functional teams to integrate AI/ML capabilities into existing platforms and develop new intelligent business efficiency and product line solutions. -Translate research insights and emerging technologies into practical solutions that can be integrated into our product lines. -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. -Ensure AI/ML solutions meet industry standards, regulatory requirements and cybersecurity protocols for critical energy infrastructure. -Foster a culture of innovation and learning within the team by encouraging experimentation with new technologies and knowledge sharing of industry developments. -Establish architectural standards, best practices and technical guidelines for AI/ML development across the CTO organization in collaboration with GEV AI/ML partners.

Requirements

• mlops
• tensorflow
• python
• phd
• edge computing
• people management

What You Bring

-Experience developing and implementing ML models using cloud MLOps pipelines such as AWS Sagemaker, Azure ML, Google VertexAI, Dataiku Cloud or equivalent. -Understanding of digital substations, IEC standards & protocols, transmission and distribution automation segment & solutions and virtualization. -Proven track record of successfully delivering complex AI/ML projects from conception to deployment. -Strong background in edge computing, IoT deployments and cloud platforms (AWS, Azure, GCP). -Experience with time-series analysis, signal processing, load forecasting and predictive modeling relevant to energy systems and grid operations. -5+ years of people management experience with demonstrated ability to lead high-performing technical teams. -PhD OR Master’s with a minimum of 5 years equivalent professional experience, in Computer Science, Electrical Engineering, Data Science or related technical field. -Proven expertise in machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.) and generative AI technologies (LLMs, SLMs, diffusion models, GANs). -Proficiency in programming languages including Python, C# or C++ as well as scientific programming + simulation tools such as MATLAB or R. -Team player, problem solver, positivity and a Can-do attitude. -Root cause analysis skills, trouble shooting and debugging skills using tools such as Wireshark, TCPDump and other Linux and Windows system tools. -Track record of applying research insights to solve real-world business problems and deliver commercial solutions; ability to balance innovation with practical implementation constraints and business requirements. -Minimum of 10 years of hands-on experience in AI/ML development with 8+ years in architectural roles -Expertise of GraphDB, SQL/NoSQL, MS Access databases. -Experience with Linux virtualized system deployment using VM, Hypervisor (EsXi, KVM, Xen, etc.), Docker and container orchestration tools. -Experience with DevOps, data pipelines, Azure ML registry, deployment methods (Docker, K8s, etc.). -Experience with reinforcement learning, optimization algorithms or control systems. -Ability to multi-task in a fast-paced multi-cultural environment. -Excellent communication, presentation, organization and documentation skills. -Excellent communication skills with ability to translate complex technical concepts to diverse audiences including leadership and non-tech stakeholders. -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 with electrical power systems T&D assets, grid automation, SCADA systems, utility and industrial customers, communications infrastructure, etc. -Understanding of industrial IoT, edge computing requirements and real-time data processing in critical infrastructure environments. -Knowledge of regulatory frameworks and cybersecurity requirements for energy sector applications. -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. -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 and evolution. -Have a combination of GPU experience, Spark and Scala to build and optimize high-performance, scalable, distributed computing solutions for data-intensive tasks; familiarity with fault tolerance and high-availability requirements for mission-critical applications.

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About Ge Vernova

-Traces roots back to Edison and Alstom, merging power, renewable, digital & financial wings. -Headquartered in Cambridge, MA, crafts large-scale gas turbines, SMRs, wind turbines, hydro and grid tech to fuel economies. -On the nuclear front, advancing small modular reactors (like BWRX‑300) in partnership with utilities and supporting semiconductor projects. -Wind prowess spans onshore, offshore and blade making—with key sites like Dogger Bank offshore and blade plants in Spain. -Electrification arm tackles grid stability: HVDC, transformers, storage, conversion, plus GridOS software powering smarter infrastructure. -Weaves finance and consulting through energy-infrastructure investments, funding solar farms to pipelines via GE Energy Financial Services.

Sector Specialisms