Senior AI/ML Applications Architect

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.

Job Description

Responsibilities:

• 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 GridNode/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.

Required Qualifications:

• 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.

Desired Characteristics:

• Proven expertise in machine learning frameworks (TensorFlow, 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 Sagemaker, Azure ML, Google VertexAI, 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 DevOps, 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 and evolution.

• Strong background in edge computing, IoT deployments and cloud platforms (AWS, Azure, GCP).

• Expertise of GraphDB, SQL/NoSQL, MS Access databases.

• Proficiency in programming languages including Python, C# or C++ as well as scientific programming + simulation tools such as MATLAB or R.

• Experience with time-series analysis, signal processing, load forecasting and predictive modeling relevant to energy systems and grid operations.

• Proven track record of successfully delivering complex AI/ML projects from conception to deployment.

• 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.

• Understanding of industrial IoT, edge computing requirements and real-time data processing in critical infrastructure environments.

Additional Information

Relocation Assistance Provided: No