Job Summary

Enterprise AI strategy & architecture leader | Multi-cloud Generative AI / Agentic AI

Job Type Full-Time

Experience 12–16 years overall; at least 5 years in Generative AI / Agentic AI with significant enterprise architecture and production delivery experience

Locations Noida, Hyderabad, Chennai, Bangalore, Pune

Primary Focus Enterprise AI strategy, solution architecture, architecture governance, technology standards and strategic technical leadership

Role Overview

We are looking for a Senior AI Architect to define the enterprise architecture and technology strategy for Generative AI and Agentic AI solutions across strategic customer programs and organizational platforms. The Senior AI Architect is the senior technical authority who establishes target architectures, makes cross-platform technology decisions, leads architecture governance, shapes reusable reference patterns, and drives complex AI programs from strategy through production at scale.

• Senior enterprise AI architect and technical authority


• Owns target-state architecture and strategic technology decisions


• Leads architecture governance and influences platform roadmap


• Provides technical leadership across architects, FDEs, engineering and customer teams

Typical time allocation

• ~55% architecture, strategy and technology decisions


• ~25% customer and executive stakeholder leadership


• ~20% architecture governance, mentoring and technical oversight

Key Responsibilities

• Own the overall AI architecture strategy for strategic enterprise programs from business vision and discovery through production, scale-out, and operational maturity.


• Lead executive and senior-technical stakeholder discussions to establish the AI vision, target architecture, investment priorities, technical roadmap, and transformation approach.


• Define enterprise reference architectures and standards for Generative AI, Agentic AI, multi-agent systems, RAG, model integration, AI platforms, and enterprise AI applications.


• Lead architecture decisions across GCP, Azure, AWS, OpenAI, and Anthropic Claude, selecting platforms and patterns based on business value, technical fit, security, residency, performance, operational model, and cost.


• Define strategic architecture for agent platforms including agent orchestration, Planner/Critic/Supervisor/Orchestrator patterns, domain agents, Skills, tools, MCP, memory/state, workflow management, and human-in-the-loop controls.


• Define enterprise AI data and knowledge architecture spanning structured/unstructured data, ingestion, processing, search, vector stores, knowledge graphs where required, retrieval, reranking, access controls, and evaluation.


• Establish architecture principles for model strategy including model selection, routing, fallback, fine-tuned/specialized models, SLM/LLM usage, context management, caching, and AI FinOps.


• Define enterprise security, Responsible AI, and governance architecture including identity, authorization, data protection, privacy, guardrails, auditability, model risk, policy enforcement, and human approvals.


• Define enterprise AgentOps and AI operations architecture covering observability, evaluation, quality management, tracing, logging, cost monitoring, incident management, model/prompt/version management, and continuous improvement.


• Define target-state architecture and phased roadmaps covering MVP, production, scale-out, platform standardization, and BAU operating model.


• Establish architecture review, design assurance, technology standards, reference patterns, and decision governance for AI programs.


• Review and approve high-level architecture, integration architecture, security architecture, deployment architecture, and major technical design decisions produced by AI Architects, FDEs, developers, and partner teams.


• Lead architectural assessment of complex customer requirements and determine where capabilities should be standardized, productized, bought, or built.


• Drive reusable architecture patterns and platform capabilities across customer programs and work with Product and Platform Engineering teams to influence roadmap priorities.


• Lead complex architecture escalations and provide direction for major production incidents, systemic reliability issues, and cross-platform technical challenges.


• Support proposals, RFPs, solution estimations, technical due diligence, POCs, business cases, and strategic customer engagements.


• Mentor AI Architects, Senior FDEs, FDEs, and engineering leads; establish architecture excellence and reusable engineering practices across the organization.


• Represent the AI architecture function in customer governance forums, architecture review boards, technology councils, and strategic planning sessions.

Skill Requirements

Must Have Skills

• Deep software engineering and enterprise solution architecture experience with the ability to guide and challenge production implementations.


• Extensive hands-on experience with Generative AI, LLMs, RAG, agentic AI, multi-agent architectures, tool use, evaluation, and enterprise AI application design.


• Strong experience with multiple AI/cloud platforms or demonstrated ability to design platform-neutral architectures across hyperscaler and model-provider ecosystems.


• Deep understanding of enterprise architecture across application, data, integration, cloud, security, networking, and operations.


• Strong experience designing enterprise AI platforms, reusable agent frameworks, integration patterns, and production operating models.


• Strong understanding of AI security, Responsible AI, governance, privacy, compliance, model risk, and enterprise controls.


• Strong experience with AI observability, evaluation, AgentOps, production reliability, and operational governance.


• Strong understanding of cloud-native architecture, Kubernetes/serverless platforms, CI/CD, Infrastructure-as-Code, and enterprise landing zones.


• Strong customer-facing leadership skills and ability to influence senior technical and business stakeholders.


• Demonstrated ability to make architecture decisions under ambiguity and communicate complex trade-offs clearly.

Preferred Skills

• Deep experience across two or more of GCP, Azure, AWS, OpenAI, or Anthropic Claude.


• Experience establishing enterprise AI reference architectures, platform standards, architecture review boards, and technology governance.


• Experience with MCP, A2A, enterprise agent interoperability, and multi-agent platform strategy.


• Experience with OpenTelemetry and AI observability/evaluation platforms such as Cloud Observability, Azure Monitor/Application Insights, CloudWatch, LangSmith, Arize, or equivalent.


• Experience with enterprise security architecture, Zero Trust, private connectivity, data residency, and regulatory requirements.


• Experience with AI FinOps, model strategy, platform consolidation, vendor evaluation, and total-cost-of-ownership analysis.


• Experience leading large-scale POCs, pilots, MVPs, production rollouts, RFPs, transformation programs, and strategic enterprise accounts.

Other Requirements

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or related field.


• 12–16 years of overall software engineering, cloud engineering, enterprise architecture, or equivalent technical experience.


• At least 5 years of practical Generative AI / Agentic AI experience, including enterprise production delivery.


• Demonstrated track record of owning architecture for multiple complex enterprise technology or AI programs.


• Demonstrated experience leading senior customer engagements, architecture governance, and cross-functional technical teams.

Key Attributes

• Strong enterprise architecture and strategic thinking combined with practical engineering judgment.


• Able to define a long-term architecture while maintaining a pragmatic path from MVP to production scale.


• Strong executive presence and ability to build trust with customers and internal leadership.


• Highly effective at resolving cross-team architectural conflicts and driving decisions to closure.


• Strong mentoring, influence, and architecture leadership capability.


• Continuously evaluates emerging AI technologies and translates relevant advances into practical enterprise architecture patterns.