NTT DATA is seeking a seasoned Private AI Architect to spearhead the design and deployment of enterprise-scale AI and Generative AI platforms. This pivotal role operates within private cloud, on-premises, and hybrid infrastructures, blending architectural expertise with presales acumen and delivery oversight. The focus is on constructing secure, scalable, and sovereign AI platforms leveraging open-source technologies, self-hosted Large Language Models (LLMs), and agentic AI systems. You will serve as a key advisor, guiding clients through AI transformation initiatives while upholding stringent data privacy, governance, and regulatory compliance standards.

Key responsibilities include hands-on design, build, configuration, and troubleshooting of enterprise-grade Private AI/GenAI platforms. You will create comprehensive design documents, platform blueprints, and reference architectures for diverse deployment scenarios. Develop and implement LLM, RAG, and agentic AI solutions, including data ingestion, vector databases, and orchestration components. Operationalize AI platforms using containerization and platform engineering best practices, and implement MLOps/LLMOps frameworks for robust model lifecycle management. Optimize self-hosted foundation models and AI workloads for production, manage RFPs, proposals, and estimations, and advise clients on AI strategy and operating models. Collaborate cross-functionally to establish foundational AI infrastructure and drive adoption through POCs and MVPs.

A minimum of 10-13 years of IT experience is required, with over 5 years specifically in AI/ML architecture. Proven success in delivering enterprise AI/GenAI solutions in private cloud, on-premises, or hybrid settings is essential. Deep expertise in self-hosted LLMs, open-source AI ecosystems, LLMs, RAG, vector databases, prompt engineering, and agentic frameworks is critical. Proficiency with MLOps/LLMOps lifecycles, Kubernetes, Docker, and distributed systems is expected. Familiarity with AI infrastructure, including GPUs, model serving, inference optimization, and data platforms is necessary. Experience with security, privacy, and sovereign AI controls, such as data isolation, zero-trust, IAM, and encryption, is also required. Strong client-facing skills, including RFP response and stakeholder management, are vital.