3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Act as the primary technical and delivery interlock for assigned AI opportunities from qualified intake through solution approval, contracting, and handover to delivery.
- Translate client business needs into secure, scalable, commercially viable, and delivery-ready AI solution architectures.
- Shape or own end-to-end architectures across application, model, data, integration, infrastructure, and security domains.
- Develop, lead, or coordinate prototypes and proofs of concept to validate feasibility, performance, data, integration, security, scalability, latency, and consumption-cost assumptions.
- Define delivery approaches, workstreams, milestones, dependencies, resource models, effort estimates, governance, and mobilisation plans.
- Validate estimates, rate-card assumptions, third-party costs, consumption charges, pricing, contribution, margin, contingency, and cost-recovery assumptions.
- Support proposals and statements of work, including scope, deliverables, assumptions, exclusions, dependencies, acceptance criteria, and technical and delivery terms.
- Assess technical feasibility, delivery readiness, commercial consistency, contractual risk, and security, privacy, legal, data, and responsible AI risks.
- Prepare and present opportunities for assurance and approval gates and maintain decision records, conditions, owners, and mitigations.
- Lead transition and handover to delivery, including the approved architecture, scope, commercial baseline, resource plan, assumptions, dependencies, risks, and client commitments.
- Contribute reusable AI architectures, delivery patterns, estimation models, proposal assets, commercial assumptions, and capability-development materials.
Requirements
- Bachelor’s degree or equivalent practical experience in computer science, engineering, data science, information systems, business, or a related field.
- Extensive experience in enterprise solution architecture, technical consulting, AI engineering, or complex technology delivery, preferably in a large-scale technology-services environment.
- Experience working across pre-sales and delivery, engaging senior clients, and translating business needs into viable technical solutions.
- Demonstrated experience designing or validating enterprise AI and generative AI solutions, including prototypes and integration with enterprise platforms and data.
- Experience with one or more major cloud platforms and their AI, data, and integration services.
- Experience defining delivery scope, work breakdown structures, resource models, effort estimates, and delivery plans.
- Experience contributing to commercial models, pricing assumptions, rate-card constructs, or business cases for complex technology engagements.
- Experience drafting or reviewing statements of work, managing technical and delivery risk, and supporting formal solution or deal-assurance reviews.
- Experience transitioning solutions into delivery and working across sales, commercial, engineering, delivery, security, legal, and partner teams.
- Advanced enterprise solution architecture knowledge across application, integration, data, infrastructure, cloud, and security domains.
- Strong understanding of AI, generative AI, and agentic AI patterns, including model selection and evaluation, RAG, prompting, grounding, data pipelines, APIs, and vector databases.
- Working knowledge of software engineering, DevOps, MLOps, and LLMOps, with the ability to assess production readiness.
- Strong understanding of security, privacy, compliance, data governance, and responsible AI considerations.
- Strong client-facing consulting, communication, stakeholder-management, analytical, decision-making, and attention-to-detail skills.
- Relevant cloud or AI certification from Microsoft Azure, Amazon Web Services, or Google Cloud Platform is preferred.
- Enterprise or solution architecture certification such as TOGAF, plus AI, security, privacy, agile, project, or service-management certifications, is advantageous.
- Experience in regulated environments and AI managed services, MLOps, or LLMOps is advantageous.
Benefits
- Hybrid working arrangement.
- NTT DATA offers a diverse, inclusive workplace with opportunities to grow, belong, and thrive.
Tech Stack
Categories
Solutions Engineering