Senior AI Solution Architect

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI Solution Architect based in Canada.

As a Senior AI Solution Architect, you will lead the design, development, deployment, and evolution of production-grade AI products and intelligent services.
You will bridge advanced AI research with secure, scalable, commercially viable solutions across enterprise, cloud, edge, IoT, robotics, and industrial environments.
The role spans the full AI lifecycle, from opportunity identification and business case development through architecture, model development, MLOps, deployment, and optimization.
You will work with cutting-edge technologies including foundation models, LLMs, VLMs, multimodal AI, agentic AI, computer vision, robotics, and physical AI.
Your work will help organizations turn emerging AI capabilities into measurable business value while meeting demanding requirements for reliability, safety, security, and governance.
You will collaborate with executives, product leaders, engineers, researchers, architects, operations teams, and customers across multidisciplinary environments.
This is a strategic technical leadership opportunity for someone who can combine deep engineering expertise with product thinking, commercial awareness, and enterprise architecture leadership.

• Lead the complete lifecycle of AI products and intelligent services, from opportunity identification and business case development through architecture, development, production deployment, monitoring, and continuous improvement.
• Translate business, operational, industrial, IoT, and robotics challenges into scalable AI-powered products, services, and technical roadmaps.
• Define solution architectures, data strategies, development approaches, and commercialization plans aligned with business objectives, enterprise architecture, cybersecurity, governance, and operational requirements.
• Coordinate multidisciplinary engineering activities to deliver AI solutions on schedule, within budget, and according to established development methodologies and standards.
• Design and implement robust data acquisition, labeling, curation, governance, validation, and evaluation strategies.
• Develop scalable AI training, fine-tuning, inference, deployment, and MLOps pipelines, including experiment tracking, dataset versioning, model registries, CI/CD, observability, drift detection, and model governance.
• Architect and deploy foundation models, LLMs, VLMs, computer vision, multimodal AI, liquid foundation models, sensor intelligence, and robotic perception solutions.
• Develop agentic AI systems capable of reasoning, planning, memory, tool use, workflow orchestration, multi-agent collaboration, and human-in-the-loop interaction.
• Integrate AI agents with enterprise applications, APIs, knowledge bases, operational systems, industrial equipment, IoT platforms, robotic systems, and edge devices.
• Design physical AI and autonomous systems incorporating perception, localization, mapping, planning, manipulation, navigation, motion control, and closed-loop decision-making.
• Develop multimodal perception and sensor-fusion solutions using cameras, LiDAR, radar, IMUs, industrial sensors, telemetry, and time-series data.
• Build Digital Twin and simulation environments to generate synthetic data, validate AI behavior, test edge cases, evaluate safety, and accelerate training and deployment.
• Optimize AI models for edge and resource-constrained environments using quantization, distillation, pruning, LoRA, PEFT, runtime optimization, graph optimization, and hardware-aware techniques.
• Deploy real-time AI solutions across cloud, embedded, GPU, NPU, NVIDIA Jetson, industrial edge, robotics, and IoT platforms while balancing latency, throughput, power, memory, and operational cost.
• Apply secure-by-design principles and ensure AI solutions meet cybersecurity, privacy, regulatory, governance, resiliency, observability, explainability, and responsible AI requirements.
• Define service architectures, operational documentation, deployment guides, runbooks, support models, and technical specifications throughout the AI lifecycle.
• Conduct post-implementation reviews and identify opportunities for optimization, automation, monetization, service improvement, and continuous innovation.
• Partner with product management, research, engineering, operations, enterprise architecture, cybersecurity, infrastructure, and executive stakeholders to influence AI strategy and accelerate time-to-market.
• Communicate complex AI concepts to technical and non-technical audiences and provide strategic recommendations, assessments, dashboards, and technical roadmaps.
• Mentor engineering teams, establish technical standards and best practices, and promote continuous improvement across AI development and service delivery.
• Ensure AI initiatives create measurable client and business value while maintaining high standards for operational excellence, service quality, security, and profitability.

Requirements:

• Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Computer Engineering, Electrical Engineering, or a related discipline; a Ph.D. is an asset.
• Extensive experience developing and deploying production-grade AI systems using Python, PyTorch, CUDA, distributed training frameworks, and modern MLOps practices .
• Demonstrated expertise in Foundation Models, LLMs, VLMs, Computer Vision, Multimodal AI, Agentic AI, and Physical AI .
• Strong knowledge of transformer architectures, attention mechanisms, representation learning, scaling strategies, distributed training, optimization, fine-tuning, prompt engineering, and model evaluation.
• Hands-on experience with robotics and simulation frameworks such as ROS/ROS 2, NVIDIA Cosmos, NVIDIA Omniverse, Isaac Sim, Isaac Lab, Gazebo, MuJoCo, or MoveIt , or equivalent technologies.
• Experience optimizing and deploying AI models at the edge using technologies such as TensorRT, ONNX Runtime, quantization, pruning, distillation, and hardware-aware optimization .
• Proven experience deploying AI solutions across cloud, edge, embedded, robotics, and industrial IoT environments.
• Strong understanding of enterprise software architecture, cloud-native systems, distributed computing, edge computing, IoT, robotics, autonomous systems, and production AI operations.
• Advanced knowledge of AI product lifecycle management, MLOps, service engineering, and enterprise service delivery.
• Strong understanding of cybersecurity, responsible AI, data governance, model governance, privacy, compliance, and safety considerations for enterprise AI.
• Strong commercial awareness and the ability to translate technical innovation into measurable business outcomes, value propositions, and viable service offerings.
• Advanced analytical, research, documentation, communication, negotiation, and stakeholder-management capabilities.
• Demonstrated ability to influence technical direction across multidisciplinary teams and communicate effectively with both technical and executive audiences.
• Strong customer focus with an emphasis on service quality, operational excellence, continuous improvement, and measurable business value.
• Ability to work effectively in high-pressure environments, manage competing priorities, and establish processes through collaboration.
• Experience with IT operations, service delivery, product lifecycle management, and service development is highly valuable.
• Bachelor’s degree or equivalent in Information Technology, Computer Science, Business, or a related field may be considered alongside relevant experience.
• Certifications such as ITIL, Scaled Agile, Kubernetes, AWS, Azure, Google Cloud, NVIDIA , or related technologies are beneficial.
• Experience in industrial automation, manufacturing, logistics, transportation, aerospace, energy, utilities, healthcare, or other mission-critical environments is an asset.
• Experience with embodied AI, world models, synthetic data, simulation-driven AI, safety-critical systems, open-source AI or robotics, publications, patents, or recognized technical innovation is desirable.

Benefits:

• Competitive annual compensation range of CAD $95,000–$145,000 , depending on work location, experience, technical expertise, and other qualifications.
• Fully remote working opportunity within Canada.
• Opportunity to work on advanced AI initiatives spanning foundation models, multimodal intelligence, agentic AI, robotics, physical AI, IoT, and edge computing.
• Exposure to emerging technologies and opportunities to transform cutting-edge AI research into production-grade enterprise solutions.
• Significant technical leadership and strategic influence across AI product development and service delivery.
• Opportunities to mentor engineering teams and shape technical standards, methodologies, and long-term AI strategy.
• Collaborative work with multidisciplinary teams across engineering, research, product, architecture, cybersecurity, operations, and executive leadership.
• Career growth opportunities within a global technology environment.
• Inclusive workplace committed to diversity, professional development, and equal opportunity.
• Opportunity to deliver solutions that create measurable business value while advancing innovation and operational excellence.