Generative AI Architect (GenAI Architect)

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.

Role Summary : Principal Generative AI Architect

Locations : Chennai, Mumbai and Pune

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)

I. Strategy & Executive Leadership

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.

CxO Advisory : 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.

II. Design & Engineering Oversight

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., LangGraph, CrewAI).

MLOps / LLMOps Mastery :

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.

III. 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., 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.