Senior AI Platform Architect - AI Centre of Excellence; GenAI & M365 Copilot

Position: Senior AI Platform Architect - AI Centre of Excellence (GenAI & M365 Copilot)
Location: Bengaluru

Senior AI Platform Architect - AI Centre of Excellence (GenAI & M365 Copilot)

Location:

Offshore (India) — with regular client-facing collaboration across time zones

Experience:

12–15 years (Platform / Cloud Architecture + GenAI delivery)

Role Overview
We are seeking a  Senior AI Platform Architect  to be a core member of the  AI Center of Excellence (AI CoE) . This role combines  deep platform and cloud architecture expertise  with hands-on Generative AI solution design, M365 Copilot enablement, and cross-functional stakeholder engagement.

The AI Platform Architect will design and deliver  enterprise-grade GenAI and Copilot solutions , define technical standards and guardrails, and work closely with the client’s  governance anchor  and cross-functional teams — including Infosec, Infrastructure, Compliance, and Legal — to ensure AI adoption is  secure, governed, and production-ready .

A key aspect of this role is  translating governance intent into enforceable architectural controls , and bridging the gap between business enablement goals and responsible AI implementation. This role requires  strong communication skills  and demonstrated experience working across geographies, business stakeholders, and technical teams in large enterprise settings.

Key Responsibilities
1. AI CoE Architecture Standards & Guardrails
Define and maintain AI architecture standards across cloud infrastructure, data platforms, applications, and GenAI / LLM usage.
Establish  governance-by-design  practices including auditability, access control, prompt logging, and AI lifecycle management.
Translate governance and compliance requirements — provided by the client’s governance anchor — into  enforceable technical controls and standard architectural patterns .
Contribute to reducing  fragmented guardrails and shadow AI usage  across the enterprise by defining clear adoption pathways and intake standards.
2. AI Policy & Governance Collaboration
Work alongside the client’s governance lead to assess existing AI-related policies, standards, and controls across cloud platforms, data tools, GenAI / Copilot SaaS tools, and developer platforms.
Identify gaps between policy intent and platform-level enforcement; translate findings into concrete architectural recommendations.
Produce governance artifacts including architecture decision records, control mapping documents, and standards documentation that bridge technical design and compliance requirements.
Support AI policy refresh and ensure alignment between updated policy intent and implementable technical guardrails.
3. AI Intake, Review & Decisioning Support
Support the operationalization of a structured AI intake and approval workflow, including standardized risk, security, and value assessment templates.
Define technical  “definition of done”  criteria for AI initiatives covering security review, data classification, model risk controls, and production readiness.
Partner with governance and compliance stakeholders to document and manage exceptions and risk acceptance in line with CoE standards.
4. Enterprise Security, Data & Platform Governance
Embed  security-by-design and Responsible AI principles  into GenAI architectures.
Ensure AI solutions align with enterprise standards for:
Data protection, classification, and sensitivity labeling (Microsoft Purview, M365 E5)
Identity, access control, and least-privilege principles (Entra , Cyber Ark)
Zero Trust alignment and network-layer AI governance
Auditability, compliance logging, and regulatory alignment
Apply Responsible AI frameworks (e.g.,  NIST AI RMF ) in solution design and governance artifact development.
Advice on third-party AI vendor and SaaS tool evaluations, including data processing risks, model provenance, and contract-level data protection implications.
5. GenAI Solution Architecture
Design and deliver production-grade GenAI solutions including:
Retrieval-Augmented Generation (RAG) and knowledge grounding patterns
Agentic and multi-step AI workflows
Enterprise copilots and AI assistants
Define patterns for  prompt orchestration, hallucination control, evaluation pipelines, and LLMOps observability  (model monitoring, prompt drift, cost…