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

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

Location:

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

Experience:

1215 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 clients 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 clients 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 clients 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 ID, CyberArk)
• 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 attribution, incident response).
• Guide teams on build vs. buy decisions for GenAI platforms and tooling.
• Review and approve solution designs prior to production deployment, ensuring alignment with CoE standards.
• (Good to have) Architect M365 Copilot deployments including Copilot Studio extensions, Graph connectors, and plugin integrations.

6. Stakeholder Engagement & Cross-functional Collaboration

• Act as the primary technical interface between the AI CoE and cross-functional teams including Infosec, Infrastructure, Compliance, Legal, and Business Enablement.
• Communicate architectural positions and governance recommendations clearly to both technical teams and senior non-technical stakeholders.
• Collaborate across geographies with distributed client teams; adapt communication style and engagement model to different stakeholder contexts.
• Represent the CoE in architecture review boards, governance committees, and executive briefings as needed.

Required Skills & Experience

Platform & Cloud Architecture Must Have

• Strong experience in platform or solution architecture roles, ideally in large enterprise or consulting environments.
• Proven ability to define target-state architectures and guide complex, cross-platform initiatives from design through delivery.
• Deep experience with cloud-native architectures; AWS strongly preferred. Azure or multi-cloud experience also valued (given M365/Copilot engagement context).
• Familiarity with IAM, secure networking, observability, cost optimization, and enterprise integration patterns.

Generative AI Must Have

• Hands-on experience designing and delivering GenAI solutions in enterprise environments, beyond POC stage into production.
• Strong understanding of LLM-based architectures, RAG, prompt engineering, agent frameworks, and evaluation methodologies.
• Experience with LLMOps practices: model versioning, monitoring, prompt drift detection, and cost governance.

M365 & Copilot Ecosystem Must Have

• Working knowledge of Microsoft Purview for data classification and DLP in the context of AI-enabled collaboration tools
• Understanding of M365 E5 security features

Stakeholder Communication & Cross-functional Delivery Must Have

• Demonstrated experience working directly with senior client stakeholders across business, security, compliance, and technology functions.
• Ability to present and defend technical positions to non-technical audiences including CISOs, compliance officers, and business leads.
• Experience delivering in cross-geo or distributed engagement models with structured documentation and async collaboration practices.

Security, Governance & Responsible AI Strong Plus

• Exposure to enterprise security architecture in regulated or governance-heavy environments.
• Familiarity with AI governance frameworks such as NIST AI RMF; awareness of EU AI Act risk tiers is a plus.
• Experience contributing to or reviewing AI/data policies, control frameworks, or compliance documentation.
• Familiarity with Responsible AI principles including fairness, transparency, explainability, and model risk management.

Enterprise Architecture Good to Have

• Exposure to enterprise architecture practices: TOGAF or similar frameworks, architecture review boards, and standards governance.
• Experience producing architecture decision records (ADRs), reference architectures, and governance standards documentation.

M365 Copilot Ecosystem Good to Have

• Practical experience with M365 Copilot deployment, governance, and extensibility including Copilot Studio, Graph connectors, and sensitivity label enforcement at the AI layer.
• Understanding of M365 E5 security features and their interaction with Copilot data access and permissions model.

Education

• Bachelors or Masters degree in Computer Science, Engineering, or a related field.
• Relevant cloud certifications (AWS SAA, Azure Solutions Architect) or AI/ML certifications are a plus.
• Microsoft certifications in M365 or Security (e.g., MS-900, SC-900, AI-102) are advantageous given engagement context.

What Success Looks Like

• GenAI solutions that are production-ready, secure, and adopted with confidence across the enterprise.
• Clear, reusable AI architecture standards and guardrails adopted consistently across delivery teams.
• Strong working relationships established with client-side governance, security, compliance, and infrastructure stakeholders.
• Governance artifacts and architectural standards that are actionable and trusted by cross-functional teams.
• Faster, safer progression of AI use cases from intake through to business value delivery.
• Reduced architectural risk, rework, and shadow AI exposure across the organization.