Join NTT DATA and make a significant impact as a Senior Advisor, Private AI Architect. We are a global innovator renowned for technical excellence and driving transformative change for our clients and society. Our inclusive culture fosters growth and belonging. This role focuses on designing and delivering enterprise-scale AI and GenAI platforms within private cloud, on-premises, and hybrid settings. You will act as a trusted advisor, leading AI transformation initiatives while prioritizing data privacy, governance, and compliance.

We seek an experienced professional with a strong background in architecture, presales, and delivery. Your expertise will be crucial in building secure, scalable, and sovereign AI platforms leveraging open-source technologies, self-hosted LLMs, and agentic AI systems. This position requires a deep understanding of enterprise AI solutions and the ability to guide clients through complex AI adoption journeys.

Key responsibilities include architecting, building, configuring, and deploying enterprise-grade Private AI/GenAI platforms. You will develop high-level designs, low-level designs, platform blueprints, and reference architectures for diverse environments. Hands-on experience in implementing LLM, RAG, and agentic AI solutions, including ingestion pipelines and vector databases, is essential. Building and operationalizing AI platforms using Kubernetes, containerization, and MLOps/LLMOps frameworks are core duties. Expertise in deploying and optimizing self-hosted foundation models, including GPU utilization and latency tuning, is also critical.

Further responsibilities involve developing reusable frameworks, leading RFP responses and proposals, and advising clients on AI strategy and operating models. Collaboration with infrastructure, cloud, security, and data engineering teams is expected, as is driving Proofs of Concept (POCs) and Minimum Viable Products (MVPs) to foster enterprise AI adoption.

We require 10 to 13 years of IT experience, with at least 5 years focused on AI/ML architecture and proven delivery of enterprise AI/GenAI solutions in private cloud, on-premises, or hybrid environments. A strong understanding of self-hosted LLMs, open-source AI ecosystems, and enterprise AI platforms is necessary. Expertise in LLMs, RAG, vector databases, prompt engineering, and agentic frameworks like LangChain or LlamaIndex is essential.

Proficiency in Kubernetes, Docker, MLOps/LLMOps lifecycle, and distributed systems is required. Experience with AI infrastructure including GPUs, model serving frameworks, and inference optimization is key. Knowledge of data platforms and enterprise integration architectures is beneficial. Familiarity with technologies such as MLflow or Kubeflow is preferred. Strong skills in stakeholder management and communication, along with experience in RFPs and proposals, are mandatory. Experience with security, privacy, and sovereign AI controls is also crucial.