AI Architecture Lead

Function: Enterprise AI

Reports to: VP / SVP - Enterprise AI

Permanent/ Temporary: Permanent

Experience:

12-15 Years

Location: Delhi NCR, Bangalore, Hyderabad, Chennai

Role Summary:

- AI and Agentic Systems and Platform Architecture, Standards Development.

- Design Connected-Secure-Governed-Scalable Enterprise and Operations Solutions Components and Platform Fabric-Bus.

- Convene AI Architecture Reviews, Reference Architectures, Evaluation of Build vs. Buy Considerations, Documentation of Choices, Subscription-Licensing Economics.

- Functional-Secure-Scalable-Governed Multi-Modal Systems, Drive Cross-Functional Reusability, Guardrails, Pipelines.

- Guide Engineering and Runtime Delivery Teams, Address Complex Architectural Challenges.

- Interface with CTO-CIO stakeholders on Architectural Deliberations.

- Deep knowledge of Leading-Edge and Emerging AI Concepts and Capabilities: Knowledge Graphs, Context Engineering, Agents Harness, and Loop Engineering.

- Understanding of Multi-Modal Ecosystem, Cloud, Data Mgmt., Responsible and Secure AI, Token Economics, AI FinOps.

Key Responsibilities:

1. Agentic Solution Architecture and Design Authority:

- Lead discovery and solutioning with stakeholders; translate business objectives into target-state agentic AI architectures, blueprints, and roadmaps.

- Own end-to-end solution design: multi-agent orchestration, tool-using agents, human-in-the-loop patterns, memory and state management, RAG and knowledge layers, and enterprise integration.

- Drive build vs. buy vs. partner decisions for models, agent frameworks, and solution with EXL standards.

2. Architecture Standards, Governance and Responsible AI:

- Define and enforce reference architectures, design standards, and reusable patterns for agentic AI solutions across accounts.

- Embed security, privacy, compliance, and responsible AI - including agent guardrails, evaluation frameworks, and auditability - into every design.

- Conduct architecture and design reviews, ensuring solutions are scalable, cost-efficient, and production-grade.

3. Technical Leadership Through Delivery:

- Guide Forward Deployment Engineers, data scientists, and delivery teams from design through production - remaining hands-on at critical points (prototyping, integration, performance tuning).

- De-risk delivery by resolving complex technical blockers: legacy integration, agent reliability, model performance, and latency-cost-quality trade-offs.

- Ensure solutions move beyond POCs to enterprise-wide adoption and value realization.

4. Stakeholder Engagement and Advisory:

- Act as trusted technical advisor to CIOs, CDOs and enterprise architects; lead architecture workshops, design authority boards, and executive briefings.

- Support pre-sales and strategic deals: solution shaping, effort estimation, technical proposals, and orals.

- Articulate architecture decisions in business terms - value, risk, cost, and time-to-market.

5. Capability Building and Reuse:

- Convert engagement learnings into reusable assets, accelerators, and reference implementations for agentic AI portfolio.

- Mentor architects and senior engineers; raise the architecture bar across the Enterprise AI practice.

- Continuously track and translate emerging AI advances (Agentic AI, LLMs, autonomous systems) into architecture strategies.

Required Experience and Qualifications:

- 12+ years of experience in software-solution architecture, data, or digital transformation, with 3+ years architecting AI-LLM or agentic AI solutions.

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

- Proven track record of architecting and delivering production AI-GenAI solutions for large enterprise clients.

- Strong understanding of Agentic AI and LLM architectures, RAG, evaluation, and guardrails.

- Experience engaging with CTOs, CIOs, enterprise architects, and executive stakeholders.

Leadership and Behavioral Expectations:

- Enterprise-first mindset with strong commercial orientation and ownership of outcomes.

- Ability to influence without authority across business organizations, delivery teams, and partners.

- Exceptional executive communication - able to explain and defend architecture decisions in business terms to C-suite audiences.

- Calm, decisive technical leadership in ambiguity, escalations, and rapid change.

- Deep commitment to responsible AI and ethical deployment.