Senior Presales AI Solutions Architect

Role: Senior AI Solutions Architect

Job Profile Summary

The Senior AI Solutions Architect is an advanced subject matter expert responsible for shaping, validating and assuring complex artificial intelligence solutions during the opportunity-to-order lifecycle.

The role operates as the primary technical and delivery interlock between regional go-to-market teams and the Global AI Delivery Unit. It enables sellers and regional solution teams to access AI delivery expertise without requiring core delivery resources to become extensively engaged in routine pre-sales activity.

The Senior AI Solutions Architect works with clients, sales teams, solution teams, commercial management, delivery leaders and specialist technical teams to translate business needs into secure, scalable, commercially viable and delivery-ready AI solutions.

The role contributes to, or leads, the development of solution architectures, prototypes and proofs of concept where required. It validates solution feasibility, delivery scope, resource requirements, effort estimates, commercial assumptions, delivery risks and contractual commitments before NTT DATA enters into a client agreement.

As part of the Global AI Delivery Unit, the role helps protect delivery outcomes and the AI Delivery P&L by ensuring that proposed solutions have an appropriate delivery structure, cost-recovery model, risk position and transition plan before customer signature.

Key Responsibilities

Deal intake and GTM interlock

• Act as the primary technical and delivery interlock for assigned AI opportunities from qualified intake through solution approval, contracting and handover to delivery.
• Engage with regional GTM, sales and solution teams early in the opportunity lifecycle to understand the clients business problem, intended outcomes, technical environment, data landscape and commercial requirements.
• Assess whether the opportunity is sufficiently qualified for Global AI Delivery engagement and identify any additional information, discovery or customer validation required.
• Review use-case descriptions and business requirements to assess whether the proposed use of AI is appropriate, feasible and aligned with measurable client outcomes.
• Support structured discovery sessions with clients and internal stakeholders, translating business requirements into actionable technical and delivery requirements.
• Identify the specialist capabilities required to support the pursuit and coordinate targeted engagement from Global AI Delivery Unit, security, legal, commercial and regional delivery teams.
• Manage the involvement of delivery specialists carefully, ensuring that delivery expertise is used for material design decisions and specialist validation without drawing delivery teams unnecessarily into general pre-sales activity.
• Maintain clear communication with opportunity owners regarding solution status, outstanding decisions, risks, dependencies and readiness for assurance.

AI solution architecture and design

• Shape or own the end-to-end architecture of AI solutions across application, model, data, integration, infrastructure and security domains.
• Evaluate architecture options and select appropriate AI models, cloud services, orchestration frameworks and third-party technologies, balancing cost, performance, scalability, security and delivery complexity.
• Define secure integration, functional and non-functional requirements, data governance and responsible AI requirements for the proposed solution.
• Align solutions with NTT DATA reference architectures, reusable assets, delivery patterns and approved technology standards.

Prototyping and feasibility validation

• Develop, lead or coordinate prototypes and proofs of concept where required to validate feasibility, reduce delivery risk or demonstrate the proposed solution.
• Test material assumptions relating to model performance, data, integration, security, scalability, latency and consumption cost, with clear success and exit criteria.
• Translate prototype findings into the production architecture, estimate, assumptions and risk position, engaging specialist engineering support where required.

Delivery planning and resource model

• Translate the solution into a realistic delivery approach covering workstreams, deliverables, milestones, dependencies and mobilisation.
• Define or validate the resource model, effort estimates, skills, location strategy and use of global delive…