Senior Gen AI Applications Architect

This is a fully remote position, open to applicants in Canada.

📋 Description

• Design and develop scalable AI/ML solutions, encompassing generative AI and agentic AI applications, aimed at grid automation, digital transformation, product-line applications, and enhancing business efficiency.

• Set architectural standards, best practices, and technical guidelines for AI/ML development within the CTO organization.

• Establish technical foundations utilizing modular and microservices architectures, cloud and edge computing patterns, API-first and privacy-by-design principles, containerization, orchestration, autoscaling, infrastructure as code, CI/CD, data and MLOps pipelines, and collaborative experimentation environments.

• Design and implement solutions on GE GridNode and other edge platforms, adhering to container and microservices principles.

• Develop strategies aimed at optimizing model performance in production environments.

• Provide guidance to project and product teams regarding generative AI and agentic AI architecture and solution methodologies.

• Define and implement monitoring, observability, and guardrails for generative AI and agentic AI solutions.

• Mentor teams in creating scalable, reliable, maintainable, production-grade systems and reusable frameworks.

• Collaborate with cross-functional teams to incorporate AI/ML capabilities into existing platforms and to create intelligent product and business efficiency solutions.

• Stay informed about advancements in AI/ML, generative AI, agentic AI, and energy-system technologies, translating these developments into actionable recommendations.

• Assess AI/ML tools, platforms, methodologies, and vendor solutions to address grid-automation challenges and business opportunities.

• Design and conduct proofs of concept to validate feasibility and support production decisions.

• Report to the Director of Data Analytics and AI Solutions within the Grid Automation CTO organization.

⛳️ Requirements

• Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline.

• A minimum of 7 years of practical experience in software engineering, AI/ML development, and/or solution architecture.

• Proven experience in building and architecting generative AI and agentic AI solutions and applications.

• Familiarity with architecting and implementing generative AI and agentic AI solutions on AWS or similar cloud infrastructures like Microsoft Azure or Google Cloud Platform.

• Proficiency with TensorFlow, PyTorch, or scikit-learn, and generative AI technologies including large and small language models, diffusion models, and GANs.

• Experience with agentic AI frameworks and architectures, such as multi-agent orchestration, tool and function calling, retrieval-augmented generation, agent memory, and planning.

• Background in implementing monitoring, observability, and guardrails for generative AI and agentic AI systems.

• Experience in applying AI/ML frameworks, AI/MLOps, and CI/CD in both cloud-native and on-premises environments within operational technology or industrial automation.

• Experience in developing and deploying ML models using cloud MLOps services like Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Dataiku Cloud, or equivalent.

• Experience in developing and testing AI/ML algorithms and grid or physics models using MATLAB, PSCAD, PSS/E, DIgSILENT PowerFactory, or similar tools.

• Familiarity with DevOps, data pipelines, model registries, Docker, and Kubernetes.

• Experience in designing solutions throughout the complete AI/ML lifecycle.

• Strong foundation in edge computing, industrial IoT, real-time processing, and cloud platforms.

• Knowledge of graph databases, SQL and NoSQL databases, and Microsoft Access.

• Proficiency in Python and at least one additional language such as C# or C++, along with experience in scientific programming or simulation tools like MATLAB or R.

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

• Demonstrated success in delivering complex AI/ML projects from inception through to production deployment.

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

🏝️ Benefits

• Discretionary annual bonus.

• Relocation Assistance Provided: No.