Senior AI Solution Architect

The Senior AI Solution Architect is a senior technical subject matter expert responsible for architecting developing deploying and continuously evolving production-grade AI products and intelligent services that accelerate business innovation operational excellence and digital transformation. This role spans the complete AI product and service lifecyclefrom opportunity identification business case development solution architecture data strategy model development validation deployment operations optimization and continuous improvementacross cloud edge IoT robotics and embodied AI environments.

The successful candidate will transform cutting-edge IIoT AI research into secure scalable commercially viable and enterprise-ready solutions while ensuring alignment with business objectives enterprise architecture cybersecurity governance operational excellence and client experience. They will serve as a technical leader partnering with product management engineering research operations enterprise architecture and executive stakeholders to define AI strategy accelerate time-to-market and establish best practices for production AI systems.

This role requires deep expertise in modern Foundation Models Large Language Models (LLMs) Vision-Language Models (VLMs) Computer Vision Agentic AI Physical AI Robotics Multimodal Intelligence and Liquid Foundation Models (LFMs). The engineer will build intelligent systems that bridge AI with enterprise software industrial automation IoT platforms sensors robots digital twins and autonomous systems operating under real-world latency safety reliability and resource constraints.

• Lead the complete lifecycle of AI products and intelligent services from concept through production and continuous optimization.
• Translate business operational industrial and robotics challenges into scalable AI-powered products and services.
• Develop business cases value propositions technical roadmaps and commercialization strategies for new AI capabilities.
• Coordinate cross-functional engineering activities to deliver AI solutions on schedule within budget and aligned with enterprise development methodologies.
• Ensure AI initiatives align with enterprise architecture cybersecurity standards governance frameworks operational requirements and strategic business priorities.
• Drive continuous improvement of AI engineering methodologies development standards and service delivery practices.
• Conduct post-deployment reviews and identify opportunities for optimization automation monetization and operational improvements.

• Own the end-to-end AI lifecycle from research and experimentation through production deployment monitoring and continuous improvement.
• Design robust data acquisition labeling curation governance validation and evaluation strategies.
• Develop scalable training fine-tuning inference and deployment pipelines.
• Establish production-grade MLOps capabilities including experiment tracking dataset versioning model registries CI/CD observability drift detection model governance and operational monitoring.
• Deliver highly available secure maintainable and scalable AI services across cloud edge embedded and hybrid infrastructures.

Design train fine-tune optimize and deploy state-of-the-art AI systems including:

• Foundation Models
• Large Language Models (LLMs)
• Vision-Language Models (VLMs)
• Computer Vision
• Multimodal AI
• Liquid Foundation Models (LFMs)
• Sensor Intelligence
• Robotic Perception

Apply advanced expertise in:

• Transformer architectures
• Self-attention and cross-attention
• Positional encoding
• Representation learning
• Scaling laws
• Distributed training
• Optimization dynamics
• Fine-tuning methodologies
• Prompt engineering and model evaluation

Design intelligent AI agents capable of:

• Autonomous reasoning
• Long-term memory
• Planning
• Tool utilization
• Workflow orchestration
• Multi-agent collaboration
• Human-in-the-loop interaction
• Autonomous execution

Develop enterprise agentic platforms integrating with:

• Enterprise applications
• APIs
• Knowledge bases
• Operational systems
• Industrial equipment
• IoT and IIoT platforms
• Robotic systems
• Edge devices

Implement governance frameworks covering:

• Safety
• Security
• Explainability
• Evaluation
• Monitoring
• Responsible AI
• Compliance
• Risk management

Develop intelligent robotic systems integrating:

• Perception
• Localization
• Mapping
• Planning
• Manipulation
• Navigation
• Motion control
• Autonomous reasoning
• Closed-loop decision making

Support solutions across:

• Industrial automation
• Manufacturing
• Warehousing
• Logistics
• Autonomous inspection
• Smart infrastructure
• Human-robot collaboration
• Critical infrastructure
• Autonomous mobile robots

Integrate modern AI with classical robotics controls engineering and safety-critical system design.

Design multimodal perception systems utilizing:

• RGB cameras
• Stereo vision
• Depth cameras
• LiDAR
• Radar
• IMUs
• Industrial sensors
• Telemetry
• Time-series data

Develop sensor fusion pipelines supporting:

• Scene understanding
• SLAM
• Localization
• Mapping
• Object tracking
• Anomaly detection
• Situational awareness
• Predictive intelligence
• Decision support

Develop Digital Twin and Sim2Real environments to:

• Generate synthetic data
• Validate AI behavior
• Evaluate safety
• Stress-test edge cases
• Reduce deployment risk
• Accelerate AI training

Optimize AI models using:

• Quantization
• Distillation
• Structured pruning
• LoRA
• PEFT
• Runtime optimization
• Compiler optimization
• Graph optimization
• Hardware-aware optimization

Deploy optimized AI across:

• Embedded platforms
• NVIDIA Jetson
• GPUs
• NPUs
• Industrial edge platforms
• Robotics platforms
• Resource-constrained IoT devices

Deliver real-time AI systems optimized for latency throughput power consumption memory utilization and operational cost.

• Apply Secure-by-Design principles throughout the AI lifecycle.
• Ensure compliance with enterprise architecture cybersecurity privacy regulatory and governance standards.
• Design AI systems emphasizing resiliency observability explainability traceability and operational reliability.
• Implement AI governance model lifecycle management access control data governance and responsible AI practices.
• Collaborate closely with cybersecurity enterprise architecture infrastructure platform engineering and operations teams.

• Develop comprehensive business technical operational and architectural documentation throughout the AI lifecycle.
• Produce service definitions solution architectures deployment guides operational runbooks and support documentation.
• Design AI services that maximize client value operational efficiency adoption and commercial outcomes.
• Ensure solutions are operationally supportable maintainable scalable and aligned with enterprise service management practices.

• Partner with executive leadership product management research engineering operations enterprise architecture and customers.
• Communicate complex AI concepts to both technical and non-technical stakeholders.
• Provide executive dashboards technical assessments and strategic recommendations.
• Mentor engineers and establish engineering standards best practices and technical roadmaps.
• Influence enterprise AI strategy and drive innovation across the organization.

The successful candidate demonstrates:

• Expert knowledge of Artificial Intelligence Machine Learning Deep Learning Foundation Models and modern transformer architectures.
• Strong understanding of enterprise software architecture cloud-native systems distributed computing edge computing IoT robotics and autonomous systems.
• Advanced knowledge of AI product lifecycle management MLOps production AI operations and service engineering.
• Strong commercial awareness with the ability to translate technical innovation into measurable business value.
• Excellent analytical documentation research communication and stakeholder management skills.
• Proven ability to influence technical direction across multidisciplinary teams.
• Strong customer focus with an emphasis on operational excellence and continuous innovation.

• Masters degree in Computer Science Artificial Intelligence Machine Learning Robotics Computer Engineering Electrical Engineering or a related discipline. A Ph.D. is considered 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 experience with robotics frameworks such as ROS/ROS 2 NVIDIA Cosmos Omniverse Isaac Sim Isaac Lab Gazebo MuJoCo MoveIt or equivalent simulation and Digital Twin platforms.
• Experience optimizing AI models for edge deployment using TensorRT ONNX Runtime quantization pruning distillation and hardware-aware optimization.
• Proven experience deploying AI solutions across cloud edge embedded robotics and industrial IoT environments.

• Experience with embodied AI world models synthetic data generation and simulation-driven AI development.
• Experience in industrial automation manufacturing logistics transportation aerospace energy utilities healthcare or other mission-critical industries.
• Experience deploying AI solutions in safety-critical or regulated environments.
• Contributions to open-source AI robotics or multimodal learning communities.
• Publications patents or recognized innovation in AI robotics or autonomous systems.
• Professional certifications in Agile ITIL Kubernetes AWS Azure Google Cloud NVIDIA or related technologies.

The ideal candidate combines the capabilities of an AI researcher machine learning engineer robotics engineer software architect systems engineer product strategist and technical leader. They excel at transforming emerging AI research into secure scalable production-ready AI platforms that deliver measurable business value while balancing innovation operational excellence governance client experience and commercial success. They are equally comfortable discussing transformer internals multimodal reasoning robotics edge AI optimization enterprise architecture MLOps and executive AI strategy while mentoring engineering teams and shaping the organizations long-term AI vision.

• Manages the coordination of activities actions and deliverables in the service development process to ensure completion within time and budget and in line with development standards and methodologies.
• Drives and evolves a robust regional inter-lock for service development aligned to the global/group methodology to ensure expedient time-to-market / release of developments.
• Ensures new service offer requirements from Service Offer Management have clearly defined business value outcomes and that requirements are substantiated by an appropriate business case and prioritized according to business impact and importance.
• Where required supports Service Offer Management in the formation of business cases for new service offers.
• Supports the identification development and implementation of service pre-design in line with agreed technical operational and architectural specifications.
• Throughout the development cycle prepare and maintain documents for service design/definition relating to the development of new services or enhancements to existing services.
• Supports with providing reports and dashboards highlighting risks issues and trends to support management decision making.
• Pays attention to the security architecture standards and requirements for the new service ensuring the secure-by-design principle.
• Ensures that the client journey is defined design and implemented optimizing client experience and maximizing economic return through new monetizable client features and capabilities.
• Use expertise to provide input into the continuous improvement of the services development methodology and its practices.
• Conduct post implementation reviews to audit the commercial operational and technical performance of new service offers and provide feedback to the business on possible improvements and optimization opportunities.

• Advanced understanding of the vast range of IT operations and companys service offerings.
• Advanced knowledge and understanding of IT industry environment and business needs.
• Excellent relationship management and demonstrated collaborative skills in working with internal and external business leaders and stakeholders.
• Excellent communication skills (verbal and written) coupled with good questioning skills.
• Advanced knowledge of the design / development of procedures and technical features required to fulfil all the elements relevant to the service.
• Advanced documentation capabilities in particular business requirements.
• Advanced analytical and research skills.
• Advanced ability to persuade negotiate and influence key stakeholders.
• A go-getter with the ability to work in high-pressure situations.
• Ability to establish and manage processes and practices through collaboration and the understanding of business.
• Service orientated individuals who are able to create client value whilst maintaining profitable business results.

• Bachelors degree or equivalent in Information Technology or Computer Science or Business or related field.
• Relevant certifications such as Scaled Agile and ITIL are beneficial.

• Advanced experience working in an Information Technology environment particularly within IT Operations.
• Advanced experience in similar role within a related environment.
• Advanced experience in effective product lifecycle management and service delivery excellence.

NTT DATA provides a reasonable range of compensation for Canada-based positions. The starting pay range for this remote role is 95000 CAD-145000 CAD. This range reflects the minimum and maximum target compensation for the position across all Canada locations. Actual compensation will depend on a number of factors including the candidates actual work location relevant experience technical skills and other qualifications.