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.

Key Responsibilities:
AI Strategy, Product & Service Development

• 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.

AI Research, Engineering & Production

• 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.

Foundation Models & Multimodal Intelligence

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

Agentic AI & Autonomous Systems

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

Physical AI, Robotics & Autonomous Systems

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.

Perception, Sensor Fusion & Digital Twins

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

Efficient AI & Edge Intelligence

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.

Enterprise AI Architecture, Security & Governance

• 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.

Service Design & Client Experience

• 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.

Stakeholder Leadership

• 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.

Knowledge & Competencies

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.

Required Qualifications

• Master's 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.

Preferred Qualifications

• 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 organization's long-term AI vision.

Your day at NTT DATA
The Senior AI Solution Architect is an advanced subject matter expert, responsible for supporting the organization's strategic goals of service revenue protection and growth, and operating profit improvement.

This role is responsible for increasing the company's ability to consistently and predictably deliver relevant, profitable and high-quality service offers and capabilities.