Senior AI Solution Architect

Accountabilities:

• 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.