Architect - AWS

1 week ago

Mumbai, India Saaki Argus and Averil Consulting Full time

As a Principal Generative AI Architect (15-20 years of experience), your role transcends technical implementation to focus on enterprise-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. #Principal Generative AI Architect #The 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 in CxO advisory , large-scale cloud architecture, and establishing Responsible AI frameworks across a complex organization. #Key Responsibilities (15-20 Years Experience) Enterprise AI Strategy: Define and own the 3-5 year Generative AI strategy and roadmap that aligns technology investments directly with corporate transformation goals and measurable business outcomes. Serve as the primary AI thought leader and 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 the enterprise 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: Drive market-facing thought leadership by contributing to white papers, patents, executive workshops, and co-developing Gen AI accelerators and offerings with strategic partners. End-to-End Gen AI Ecosystem Design: Architect and oversee the implementation of modular, reusable, and scalable AI platforms that integrate LLMs, Agentic AI, and multi-modal models into core enterprise systems. LLM/Agentic Architecture: Define the technical blueprints for complex AI systems, including Retrieval-Augmented Generation (RAG) pipelines, vector database strategies, and multi-agent orchestration frameworks (e.g., Architect and enforce best-in-class LLMOps pipelines for CI/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. Risk, Compliance, & Team Leadership Responsible AI & Security: Architect and enforce a robust AI 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., 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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