Generative AI Architect (GenAI Architect)

As aPrincipal Generative AI Architect(15-20 years of experience), your role transcends technical implementation to focus onenterprise-wide AI strategy, governance, and executive leadership.You are responsible for transforming business goals into a comprehensive, secure, and scalable Gen AI architecture that delivers measurable ROI.Role Summary: Principal Generative AI Architect Locations: Chennai, Mumbai and PuneThe Principal Generative AI Architect is a strategic leader responsible for defining and implementing the enterprise-wide Gen AI ecosystem. This executive-level role demands deep technical mastery of Large Language Models (LLMs) and Agentic AI, combined with proven experience inCxO advisory , large-scale cloud architecture, and establishingResponsible AIframeworks across a complex organization.Key Responsibilities (15-20 Years Experience)I. Strategy & Executive LeadershipEnterprise AI Strategy:Define and own the3-5 year Generative AI strategy and roadmapthat aligns technology investments directly with corporate transformation goals and measurable business outcomes. CxO Advisory:Serve as the primaryAI thought leaderand technical advisor to C-level executives (CTO, CIO, CDO), translating complex Gen AI capabilities and risks into clear business narratives and investment proposals. Architecture Governance:Establish theenterprise AI architecture framework , standards, and best practices for Gen AI solution design, build-vs-buy decisions, and tool selection across all business units. Innovation & GTM:Drivemarket-facing thought leadershipby contributing to white papers, patents, executive workshops, and co-developingGen AI acceleratorsand offerings with strategic partners.II. Design & Engineering OversightEnd-to-End Gen AI Ecosystem Design:Architect and oversee the implementation ofmodular, reusable, and scalable AI platformsthat integrate LLMs, Agentic AI, and multi-modal models into core enterprise systems. LLM/Agentic Architecture:Define the technical blueprints for complex AI systems, includingRetrieval-Augmented Generation (RAG)pipelines, vector database strategies, andmulti-agent orchestrationframeworks (e.g., LangGraph, CrewAI). MLOps/LLMOps Mastery:Architect and enforce best-in-class LLMOps pipelines forCI/CD, automated fine-tuning (SFT, LoRA), performance monitoring, observability , and cost governance for large-scale production models. Infrastructure & Cloud Strategy:Drive the optimal cloud and GPU/TPU resource strategy, ensuring that the AI infrastructure (AWS, Azure, or GCP) supports efficient training and low-latency inference at scale.III. Risk, Compliance, & Team LeadershipResponsible AI & Security:Architect and enforce a robustAI Governance framework(e.g., aligned with NIST AI RMF or EU AI Act) to ensure ethical use, compliance, data privacy, model explainability, and mitigation of bias and security risks (e.g., adversarial attacks). Cross-Functional Leadership: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. Vendor & Partner Management:Manage strategic relationships with cloud hyperscalers (AWS, Azure, GCP) and Gen AI platform providers, ensuring their roadmaps align with the organization’s long-term architectural needs.

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