AI & Agentic Platform Architect Lead

Location

Kochi

Other locations

Anywhere in Country

Salary

Competitive

Date

24 Sept 2026

Job description

Requisition ID

1739561

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

The opportunity

We are seeking an accomplished Associate Director – AI & Agentic Platform Architect to lead the architecture, engineering and operational evolution of enterprise-scale AI platforms that drive intelligent operations and digital employee experiences.

This role spans the complete Build Deploy Operate Improve lifecycle and requires deep expertise across Agentic AI, Generative AI, Enterprise Application Architecture, Data & Knowledge Platforms, Automation, Observability and AI Operations.

As a key technology leader, you will be responsible for defining the enterprise architecture strategy for AI-powered platforms, designing scalable and resilient solutions, and ensuring successful adoption and operation of AI capabilities at enterprise scale. You will work across business, engineering, architecture and operations teams to transform emerging AI technologies into secure, governed and production-ready solutions that deliver measurable business value.

Success in this role requires a proven ability to architect, build and operate secure, resilient and highly available enterprise products at scale while driving innovation, operational excellence and enterprise adoption of AI-powered solutions.

Your key responsibilities

Own the end-to-end architecture vision, standards and technical strategy for enterprise AI and Agentic AI solutions.

Design single-agent and multi-agent architectures encompassing orchestration, reasoning, collaboration, memory, context management, tool integration and human-in-the-loop controls.

Architect AI-powered operational capabilities that observe, correlate, diagnose, predict, prevent and resolve technology issues.

Design intelligent conversational experiences capable of understanding user and technology context, diagnosing issues and safely initiating actions.

Architect enterprise knowledge and intelligence platforms leveraging structured and unstructured data, operational telemetry, Retrieval-Augmented Generation (RAG), semantic and hybrid retrieval, and knowledge graph technologies.

Define Generative AI architecture covering model strategy, model routing, grounding, context engineering, evaluation frameworks, guardrails and performance optimization.

Architect scalable enterprise applications utilizing distributed systems, microservices, APIs, event-driven architectures and cloud-native engineering patterns.

Establish reusable integration patterns across enterprise applications, operational systems, data platforms and automation services.

Define architecture standards for identity, security, privacy, Responsible AI, explainability, auditability and governance.

Establish engineering standards covering scalability, availability, resilience, performance, maintainability and security.

Lead architecture governance, technology evaluations, design reviews and strategic build-versus-buy decisions.

Provide technical leadership, mentorship and guidance across architecture, engineering and platform teams.

Provide technical leadership across the full product lifecycle, from engineering and release management through production operations and continuous improvement.

Ensure enterprise AI services consistently meet availability, reliability, performance, scalability and service-level objectives.

Establish end-to-end observability across applications, infrastructure, data platforms, AI models and agent workflows.

Drive incident management, problem management and reliability engineering practices, including root-cause analysis and prevention of recurring production issues.

Establish operational readiness frameworks covering release governance, capacity planning, resilience validation and disaster recovery.

Define and implement AIOps, LLMOps and AgentOps practices covering quality, performance, latency, evaluation, telemetry, failure management and cost optimization.

Utilize operational insights to continuously improve platform architecture, engineering quality, user experience and business outcomes.

Engage, influence and manage senior technology and business stakeholders, including CTO-level leaders, to align AI platform strategy, architecture decisions and delivery priorities with enterprise objectives.

Lead, mentor and develop high-performing teams, providing people leadership, coaching, performance guidance and capability-building across architecture, engineering and platform functions.

Skills and attributes for success

Deep expertise in enterprise architecture, modern application engineering and AI platform design.

Strong understanding of Agentic AI, Generative AI, multi-agent systems, orchestration frameworks, memory architectures, tool integration and AI evaluation methodologies.

Proven ability to architect and operate large-scale, highly available and resilient enterprise platforms.

Expertise in enterprise application architecture, distributed systems, APIs, event-driven architectures and cloud-native engineering.

Strong understanding of enterprise knowledge platforms, Retrieval-Augmented Generation (RAG), semantic retrieval, hybrid retrieval and knowledge graph architectures.

Experience establishing observability, reliability engineering, AIOps, LLMOps and AgentOps practices for enterprise platforms.

Strong understanding of enterprise security, identity management, privacy, governance and Responsible AI principles.

Exceptional problem-solving and systems-thinking capabilities with the ability to balance innovation, governance and operational excel