Senior AI Solution Architect
J
JobgetherSecurity & IT
CanadaFull-TimeSenior
Salary95,000 - 145,000 CAD per year
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Job Details
- Required Skills
- PythonPyTorchMLOpsComputer Vision
Requirements
- Master's degree in Computer Science, AI, Machine Learning, Robotics, or related discipline (Ph.D. is an asset).
- Extensive experience developing and deploying production-grade AI systems using Python, PyTorch, and CUDA.
- Demonstrated expertise in Foundation Models, LLMs, VLMs, Computer Vision, Multimodal AI, Agentic AI, and Physical AI.
- Hands-on experience with robotics and simulation frameworks (e.g., ROS/ROS 2, NVIDIA Omniverse, Isaac Sim, Gazebo).
- Experience optimizing AI models for edge environments using TensorRT, ONNX Runtime, and quantization.
- Proven experience deploying AI solutions across cloud, edge, embedded, and industrial IoT environments.
- Strong understanding of enterprise architecture, distributed computing, and MLOps practices.
- Solid knowledge of cybersecurity, data governance, and model safety standards.
- Advanced analytical, stakeholder-management, and technical communication capabilities.
- Ability to influence technical direction across multidisciplinary teams.
Responsibilities
- Lead the complete lifecycle of AI products and intelligent services, from opportunity identification through production deployment and monitoring.
- Translate industrial and IoT challenges into scalable AI-powered products and technical roadmaps.
- Define solution architectures, data strategies, and commercialization plans aligned with enterprise governance and cybersecurity requirements.
- Develop scalable AI training, fine-tuning, inference, and MLOps pipelines including CI/CD and model governance.
- Architect and deploy foundation models, LLMs, computer vision, and multimodal AI solutions.
- Develop agentic AI systems capable of reasoning, planning, and multi-agent collaboration.
- Integrate AI agents with enterprise applications, industrial equipment, IoT platforms, and robotic systems.
- Design physical AI and autonomous systems incorporating perception, localization, and motion control.
- Optimize AI models for edge environments using techniques like quantization, pruning, and hardware-aware optimization.
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