Generative AI Architect (GenAI Architect)

Role Overview: As a Principal Generative AI Architect with 15-20 years of experience, your role goes beyond technical implementation to focus on enterprise-wide AI strategy, governance, and executive leadership. You will be responsible for transforming business objectives into a comprehensive, secure, and scalable Gen AI architecture that delivers measurable ROI. This executive-level position requires deep technical expertise in Large Language Models (LLMs) and Agentic AI, along with a track record in CxO advisory, large-scale cloud architecture, and the establishment of Responsible AI frameworks within a complex organization. Key Responsibilities: - Strategy & Executive Leadership - Define and own the 3-5 year Generative AI strategy and roadmap aligning technology investments with corporate transformation goals and measurable business outcomes. - Serve as the primary AI thought leader and technical advisor to C-level executives, translating complex Gen AI capabilities and risks into clear business narratives and investment proposals. - Establish enterprise AI architecture framework, standards, and best practices for Gen AI solution design, build-vs-buy decisions, and tool selection across all business units. - Drive market-facing thought leadership through white papers, patents, executive workshops, and co-developing Gen AI accelerators and offerings with strategic partners. - Design & Engineering Oversight - Architect and oversee the implementation of modular, reusable, and scalable AI platforms integrating LLMs, Agentic AI, and multi-modal models into core enterprise systems. - Define technical blueprints for complex AI systems, including Retrieval-Augmented Generation (RAG) pipelines, vector database strategies, and multi-agent orchestration frameworks. - Architect and enforce best-in-class LLMOps pipelines for CI/CD, automated fine-tuning, performance monitoring, observability, and cost governance for large-scale production models. - Drive the optimal cloud and GPU/TPU resource strategy to support efficient training and low-latency inference at scale. - Risk, Compliance, & Team Leadership - Architect and enforce a robust AI Governance framework to ensure ethical use, compliance, data privacy, model explainability, and mitigation of bias and security risks. - Lead, mentor, and build a high-performing community of Gen AI Architects, Data Scientists, and MLOps Engineers, fostering a culture of technical excellence and continuous learning. - Manage strategic relationships with cloud hyperscalers and Gen AI platform providers, ensuring their roadmaps align with the organization's long-term architectural needs.,