Architecture Lead - Enterprise AI

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.