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About the Role

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 lifecycle—from opportunity identification, business case development, solution architecture, data strategy, model development, validation, deployment, operations, optimization, and continuous improvement—across 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.

Responsibilities

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, and 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, and Autonomous execution.
Develop enterprise agentic platforms integrating with Enterprise applications, APIs, Knowledge bases, Operational systems, Industrial equipment, IoT and IIoT platforms, Robotic systems, and Edge devices.
Implement governance frameworks covering Safety, Security, Explainability, Evaluation, Monitoring, Responsible AI, Compliance, and Risk management.
Develop intelligent robotic systems integrating Perception, Localization, Mapping, Planning, Manipulation, Navigation, Motion control, Autonomous reasoning, and Closed-loop decision making.
Support solutions across Industrial automation, Manufacturing, Warehousing, Logistics, Autonomous inspection, Smart infrastructure, Human-robot collaboration, Critical infrastructure, and 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, and Time-series data.
Develop sensor fusion pipelines supporting Scene understanding, SLAM, Localization, Mapping, Object tracking, Anomaly detection, Situational awareness, Predictive intelligence, and Decision support.
Develop Digital Twin and Sim2Real environments to Generate synthetic data, Validate AI behavior, Evaluate safety, Stress-test edge cases, Reduce deployment risk, and Accelerate AI training.
Optimize AI models using Quantization, Distillation, Structured pruning, LoRA, PEFT, Runtime optimization, Compiler optimization, Graph optimization, and Hardware-aware optimization.
Deploy optimized AI across Embedded platforms, NVIDIA Jetson, GPUs, NPUs, Industrial edge platforms, Robotics platforms, and 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.
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.
Supports the identification, development and implementation of service pre-design in line with agreed technical, operational and architectural specifications.
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.

Requirements

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.
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.
Advanced understanding of the vast range of IT operations and company's 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.
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.

Skills

Foundation Models
Large Language Models (LLMs)
Vision-Language Models (VLMs)
Computer Vision
Agentic AI
Physical AI
Robotics
Multimodal Intelligence
Liquid Foundation Models (LFMs)
Transformer architectures
Self-attention and cross-attention
Positional encoding
Representation learning
Scaling laws
Distributed training
Optimization dynamics
Fine-tuning methodologies
Prompt engineering
Model evaluation
Autonomous reasoning
Long-term memory
Planning
Tool utilization
Workflow orchestration
Multi-agent collaboration
Human-in-the-loop interaction
Autonomous execution
Enterprise applications
APIs
Knowledge bases
Operational systems
Industrial equipment
IoT platforms
IIoT platforms
Robotic systems
Edge devices
Safety
Security
Explainability
Monitoring
Responsible AI
Compliance
Risk management
Perception
Localization
Mapping
Manipulation
Navigation
Motion control
Closed-loop decision making
Industrial automation
Manufacturing
Warehousing
Logistics
Autonomous inspection
Smart infrastructure
Human-robot collaboration
Critical infrastructure
Autonomous mobile robots
Controls engineering
Safety-critical system design
Sensor Fusion
Digital Twins
RGB cameras
Stereo vision
Depth cameras
LiDAR
Radar
IMUs
Industrial sensors
Telemetry
Time-series data
Scene understanding
SLAM
Object tracking
Anomaly detection
Situational awareness
Predictive intelligence
Decision support
Sim2Real environments
Synthetic data generation
Quantization
Distillation
Structured pruning
LoRA
PEFT
Runtime optimization
Compiler optimization
Graph optimization
Hardware-aware optimization
Embedded platforms
NVIDIA Jetson
GPUs
NPUs
Industrial edge platforms
Robotics platforms
Resource-constrained IoT devices
Latency optimization
Throughput optimization
Power consumption optimization
Memory utilization optimization
Operational cost optimization
Secure-by-Design
Enterprise architecture
Cybersecurity
Privacy
Regulatory compliance
Governance standards
Resiliency
Observability
Traceability
Operational reliability
Model lifecycle management
Access control
Data governance
Service definitions
Solution architectures
Deployment guides
Operational runbooks
Support documentation
Client value maximization
Operational efficiency
Adoption maximization
Commercial outcomes
Operational supportability
Maintainability
Scalability
Enterprise service management
Executive leadership partnership
Product management partnership
Research partnership
Engineering partnership
Operations partnership
Enterprise architecture partnership
Customer partnership
Technical communication
Non-technical communication
Executive dashboards
Technical assessments
Strategic recommendations
Mentoring engineers
Engineering standards
Best practices
Technical roadmaps
Enterprise AI strategy influence
Innovation driving
Python
PyTorch
CUDA
Distributed training frameworks
MLOps practices
ROS/ROS 2
NVIDIA Cosmos
Omniverse
Isaac Sim
Isaac Lab
Gazebo
MuJoCo
MoveIt
TensorRT
ONNX Runtime
Agile
ITIL
Kubernetes
AWS
Azure
Google Cloud
NVIDIA
Embodied AI
World models
Simulation-driven AI development
Industrial automation
Manufacturing
Logistics
Transportation
Aerospace
Energy
Utilities
Healthcare
Mission-critical industries
Safety-critical environments
Regulated environments
Open-source contributions
AI research
Robotics research
Multimodal learning research
Publications
Patents
Innovation
IT operations
Service offerings
IT industry environment
Business needs
Relationship management
Collaborative skills
Business leaders
Stakeholders
Verbal communication
Written communication
Questioning skills
Design procedures
Development procedures
Technical features
Documentation
Business requirements
Analytical skills
Research skills
Persuasion
Negotiation
Influence
High-pressure situations
Process management
Practice management
Client value creation
Profitable business results
Information Technology
Computer Science
Business
Product lifecycle management
Service delivery excellence

Location

Remote

Work Type

Remote Working
Full-time

Experience Level

Senior
Advanced

Education Level

Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Robotics, Computer Engineering, Electrical Engineering, or a related discipline.
Ph.D. is considered an asset.
Bachelor’s degree or equivalent in Information Technology or Computer Science or Business or related field.

Salary/Compensations

95,000 CAD-145,000 CAD

Benefits

Global Top Employer status
Access to a robust ecosystem of innovation centers
Access to established and start-up partners
Investment in R&D by NTT Group

About the Company

NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100.
We are committed to accelerating client success and positively impacting society through responsible innovation.
We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services.
Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future.
As a Global Top Employer, we have experts in more than 70 countries.
NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D.

Equal Opportunity

NTT DATA is proud to be an Equal Opportunity Employer with a global culture that embraces diversity.
We are committed to providing an environment free of unfair discrimination and harassment.
We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status, or any other protected category.
Join our growing global team and accelerate your career with us.