Senior AI/ML Applications Architect

GE Vernova; Markham; On-site; CAD 100,000 - 130,000; Full time

Job summary

A leading energy solutions company is looking for an Applications Architect to lead the design of AI and machine learning systems. This role requires in-depth technical expertise and collaboration with teams to drive innovation and efficiency. The ideal candidate will have a Bachelor's degree in a relevant field and at least 7 years of experience in AI/ML development. The position is based in York Region, Markham, Canada, and offers opportunities for impactful project leadership.

Qualifications

• Minimum 7 years of experience in software engineering or AI/ML development.
• Experience with grid/physics models using simulation tools.
• Proven track record of delivering AI/ML projects from conception to deployment.

Responsibilities

• Design scalable AI/ML solutions for grid automation.
• Establish architectural standards and best practices.
• Collaborate with cross-functional teams to integrate AI/ML capabilities.

Skills

Machine learning frameworks

Generative AI technologies

AI/MLOps

DevOps

Programming languages (Python, C#, C++)

Real-time data processing

Cloud platforms (AWS, Azure, GCP)

Education

Bachelor's degree in Computer Science, Electrical Engineering, or Data Science

Tools

TensorFlow

PyTorch

MATLAB

Power system analysis software (PSS/E, Digsilent)

AWS Sagemaker

Docker

Job description

Job Description Summary

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.

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