AI Architecture Lead

Function:

Enterprise AI

Reports to:

VP / SVP Enterprise AI

Permanent/ Temporary:

Permanent

Experience:

12+ Years

Location:

Delhi/NCR, Bangalore, Hyderabad, Chennai

Role Summary:

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

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

- Convene AI Architecture Reviews, Reference Architecture(s), 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 & 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 & Secure AI, Token Economics, AI FinOps.

Key Responsibilities:

- 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.

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

- Embed security, privacy, compliance, and responsible AI into every design.

- Guide Forward Deployment Engineers, data scientists, and delivery teams from design through production.

- Act as trusted technical advisor to CIOs, CDOs and enterprise architects.

Technical Expertise:

- Multi-agent system design: supervisorworker hierarchies, plannerexecutor and reflection loops.

- Agent state, memory & context engineering: short-term vs. episodic vs. semantic memory design.

- Framework depth: LangGraph, CrewAI, AutoGen/Semantic Kernel, MCP.

- Model strategy: model portfolio design and routing, token economics, RAG, GraphRAG.

- Evaluation architecture: golden datasets, LLM-as-judge, regression harnesses.

- Guardrails: prompt-injection defenses, PII detection, policy engines.

Required Experience:

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

- Bachelors or Masters degree in Computer Science, Engineering, or a related field.

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

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