Principal Enterprise AI Architect – Strategic Advisory & Governance
Number of Positions: 2
Location: India
Engagement Type: Full-time, embedded with client team
Role Overview
We are seeking a Principal Enterprise AI Architect to serve as the strategic architecture authority for the client's enterprise AI transformation initiatives.
This is not primarily an implementation or hands-on engineering role.
The architect will operate at the enterprise level, providing independent architectural guidance across multiple AI/GenAI use cases, delivery partners, technology teams and enterprise platforms.
The individual will work closely with client AI leadership, enterprise architecture, data, technology and business stakeholders to establish a coherent, scalable and reusable AI architecture strategy across the organization.
The role will be responsible for reviewing and challenging solution architectures proposed by multiple delivery teams and partners, identifying duplication and technical debt, defining enterprise AI architecture standards, determining appropriate use of reusable platform services, and making architecture recommendations and Go/No-Go decisions.
The successful candidate will combine:
Enterprise Architecture + AI/GenAI Architecture + Strategic Advisory + Architecture Governance + Multi-Partner Leadership.
Key Responsibilities
1. Enterprise AI Architecture & Strategy
- Act as the senior architecture authority across enterprise AI and GenAI initiatives.
- Develop and maintain the enterprise AI target architecture and architecture roadmap.
- Translate business and strategic AI objectives into scalable enterprise architecture principles and patterns.
- Define standardized AI architecture patterns that can be reused across multiple business use cases.
- Establish principles for model selection, RAG, agentic AI, orchestration, enterprise data integration and AI platform services.
- Ensure AI solutions align with enterprise architecture, security, data, integration and infrastructure strategies.
2. Architecture Governance Across Multiple Delivery Partners
- Review solution architectures proposed by multiple implementation partners and internal delivery teams.
- Assess whether proposed architectures align with enterprise AI standards and reference architectures.
- Identify architectural inconsistencies, duplication, unnecessary technology proliferation and technical debt.
- Challenge solution designs when they do not provide sufficient scalability, reuse, security, performance or cost efficiency.
- Provide architecture approval recommendations and Go/No-Go decisions where appropriate.
- Influence delivery teams and partners without direct reporting authority.
- Establish mechanisms for ongoing architecture compliance across independent delivery teams.
3. AI Platform & Reusable Architecture
- Evaluate existing AI Foundry/platform capabilities and determine where they should be reused versus where new components are justified.
- Identify opportunities to consolidate duplicate AI capabilities into reusable enterprise services.
- Define reusable reference architectures and architectural building blocks.
- Establish common patterns for:
- RAG
- AI agents
- LLM orchestration
- document processing
- enterprise search
- vector search
- model access
- AI gateways
- evaluation
- observability
- security and governance
- Ensure that individual AI projects do not create unnecessary platform duplication or long-term technical debt.
4. Generative AI Architecture Advisory
Provide architectural oversight across GenAI use cases, including:
- Retrieval-Augmented Generation (RAG)
- Agentic AI
- Multi-agent architectures
- LLM orchestration
- Enterprise knowledge assistants
- Intelligent document processing
- AI-powered automation
- Enterprise search and knowledge retrieval
- AI-enabled business applications
The architect should be capable of evaluating and challenging technical approaches involving:
- Chunking strategies
- Embedding models
- Semantic search
- Keyword search
- Hybrid retrieval
- Re-ranking
- Context management
- Prompt engineering and prompt brittleness
- Model selection
- Agent orchestration
- Retrieval optimization
- Token optimization
The expectation is architectural depth sufficient to challenge implementation teams, rather than day-to-day development or coding.
5. AI Evaluation, Observability & Governance
Define enterprise standards for evaluating and monitoring AI systems.
Establish architectural approaches for measuring:
- Response quality
- Retrieval accuracy
- Hallucination
- Relevance
- Latency
- Reliability
- Model performance
- Cost per interaction
- Token consumption
- Retrieval effectiveness
- Agent performance
Define appropriate AI observability and evaluation frameworks and ensure these are incorporated into production architectures.
6. Enterprise Data & Integration Architecture
Provide architectural oversight for integration between AI solutions and enterprise platforms including, but not limited to:
- Databricks
- SharePoint
- SAP / ERP platforms
- Enterprise data lakes and warehouses
- CRM platforms
- APIs
- Microservices
- Enterprise integration platforms
- Document repositories
Ensure AI architecture aligns with enterprise data governance, security, integration and information architecture principles.
7. Architecture Review & Decision Making
- Lead architecture review sessions across multiple AI initiatives.
- Evaluate competing technology and architecture approaches.
- Identify fit-for-purpose solutions versus solutions that introduce unnecessary technical debt.
- Establish architecture decision records and governance mechanisms.
- Provide independent technical recommendations to senior client leadership.
- Escalate architectural risks and dependencies that may affect enterprise AI strategy.
8. Executive & Strategic Advisory
- Provide technical recommendations and architecture visibility to VP, Senior Director, CTO/CIO and other executive stakeholders.
- Translate highly complex AI architecture issues into clear business and strategic recommendations.
- Participate in executive architecture reviews, PI Planning and transformation governance forums.
- Help leadership understand the trade-offs between technology choices, scalability, cost, risk and time-to-market.
- Convert ambiguous AI objectives into structured architecture strategies and actionable roadmaps.
9. Multi-Team Leadership & Partner Enablement
- Coordinate architecture across multiple delivery partners and internal teams.
- Establish common architectural language and standards across teams.
- Coach solution architects and technical leads on enterprise AI architecture patterns.
- Facilitate resolution of architecture conflicts between delivery teams.
- Drive alignment without direct managerial authority.
- Build consensus while maintaining enterprise architecture standards.
10. AI Cost & Technology Optimization
Provide architecture-level guidance to optimize the economics of enterprise AI.
Evaluate:
- Model selection
- Model routing
- Token consumption
- Prompt optimization
- Retrieval strategies
- Caching
- Embedding strategies
- Inference architecture
- Model usage patterns
- Reusable AI services
Ensure that AI solutions are designed for enterprise-scale cost efficiency, not merely functional success.
Required Qualifications
Enterprise Architecture
- 15+ years of overall technology experience, with significant experience in solution, enterprise or technology architecture.
- Demonstrated experience operating as a Principal Architect, Enterprise Architect, Chief Architect or equivalent strategic architecture role.
- Strong experience defining enterprise architecture standards, reference architectures, technology strategies and architectural roadmaps.
- Experience working across multiple business domains, technology teams and transformation programs.
- Proven ability to make architecture recommendations and influence technology decisions at enterprise level.
AI / GenAI Architecture
- 4+ years of meaningful AI/ML/GenAI architecture experience, preferably within enterprise environments.
- Strong understanding of:
- Generative AI
- LLM architecture
- RAG
- Agentic AI
- AI orchestration
- Enterprise AI platforms
- AI evaluation
- AI observability
- Strong architectural understanding of RAG pipelines, including:
- chunking
- embeddings
- semantic retrieval
- keyword retrieval
- hybrid search
- re-ranking
- context optimization
- Understanding of multi-agent and agent orchestration architectures.
- Experience defining enterprise AI reference architectures and reusable AI patterns.
Enterprise Data & Integration
- Strong understanding of enterprise data architecture and integration patterns.
- Experience integrating AI solutions with enterprise platforms such as Databricks, SharePoint, ERP/SAP, CRM, data lakes, APIs and enterprise application ecosystems.
- Broad understanding of cloud architecture across one or more major cloud platforms, with preference for candidates who can operate across technology ecosystems rather than being tied exclusively to a single cloud platform.
Strategic Advisory & Governance Qualifications
The successful candidate must demonstrate experience beyond solution implementation.
Required experience includes:
- Architecture governance across multiple programs or delivery teams.
- Working with multiple implementation partners/vendors simultaneously.
- Reviewing and challenging partner solution architectures.
- Establishing enterprise architecture standards and reusable patterns.
- Identifying and eliminating architectural duplication.
- Making or influencing architecture approval / Go-No-Go decisions.
- Influencing teams without direct reporting authority.
- Working with senior business and technology leadership.
- Presenting architecture recommendations to VP/Sr. Director/CIO/CTO-level stakeholders.
- Coaching and mentoring architects and technical leads.
- Managing architectural disagreements and organizational complexity.
- Translating ambiguous business objectives into structured architecture strategies.
Preferred Experience
- Experience in a large enterprise, global system integrator, consulting organization or Fortune 500 environment.
- Experience establishing or governing an AI Center of Excellence / AI Architecture Board / Enterprise AI Platform.
- Experience working across multiple AI use cases simultaneously.
- Experience with AI platform/foundry strategy and reusable enterprise AI services.
- Experience with architecture review boards and technology governance.
- Experience working across multiple cloud platforms is preferred.
- Experience in regulated or highly complex enterprise environments is a plus.
What This Role Is NOT
This position is not primarily intended for:
- AI/ML Engineers
- GenAI Developers
- Prompt Engineers
- Cloud Architects focused primarily on infrastructure implementation
- Azure/AWS/GCP implementation specialists
- Solution Architects whose experience is primarily hands-on delivery of individual AI projects
- Architects who have worked on AI PoCs but have limited enterprise architecture or governance experience
Candidates should demonstrate architecture leadership and strategic advisory experience in addition to technical AI depth.
Pay: ₹4,000,000.00 - ₹7,000,000.00 per year
Work Location: Remote
