NOIDA · FULLTIME
DE - AE- AI Architect-SM-GDS04
Ernst Young
Noida · onsite · Posted 4d ago
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Undisclosed15–20 yrsfulltimeNoida
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Section · 01
About this role
Job Summary
We are seeking a Senior AI Architect who combines deep Java/J2EE architecture expertise with hands-on AI and Generative AI delivery experience. The role will shape AI-first enterprise solutions, embed intelligent capabilities into existing Java estates, and guide clients from experimentation through secure, production-scale adoption.
The successful candidate will operate at the intersection of enterprise architecture, product engineering, cloud-native modernization and applied AI. They will define target architectures, make technology decisions, lead engineering teams, and advise senior stakeholders on how AI can create measurable business and engineering outcomes.
Experience - 15+ years overall; 8+ years in Java/J2EE; 2+ years in AI/GenAI delivery
AI-led modernization of enterprise Java platforms and creation of scalable, secure AI solutions
The opportunity Key responsibilities AI and solution architecture - Define end-to-end architectures for AI-enabled enterprise applications, from data ingestion and retrieval through model orchestration, inference, APIs, user experience and production operations.
- Architect Retrieval-Augmented Generation (RAG), semantic search, knowledge graph, multimodal and agentic AI solutions aligned to business workflows.
- Design multi-agent systems / skills, tool-use patterns and orchestration approaches, including Model Context Protocol (MCP) where relevant.
- Establish architecture blueprints, reusable patterns, non-functional requirements and technology guardrails for scalable, secure and responsible AI adoption.
- Evaluate models, frameworks, data stores and cloud AI services based on quality, latency, security, portability, operability and cost.
- Architect enterprise solutions using Java/J2EE, Spring Boot, Spring MVC, Spring Security and RESTful APIs.
- Integrate LLM-powered services, copilots and AI agents into existing Java applications and business workflows through well-governed APIs and event-driven patterns.
- Decompose monolithic applications into modular services and define modernization pathways that introduce AI without compromising business continuity.
- Design microservices, event-driven and distributed architectures using technologies such as Kafka or RabbitMQ, container platforms and API management.
- Guide engineering teams on code quality, design patterns, performance, resilience, observability, security and maintainability.
- Design and deploy AI solutions on Azure, AWS or GCP using appropriate managed AI services and cloud-native components.
- Establish CI/CD, DevSecOps, MLOps and LLMOps practices covering prompt, model and configuration versioning; automated testing; deployment; monitoring; evaluation and rollback.
- Define AI quality and evaluation frameworks for relevance, groundedness, safety, latency, reliability and cost.
- Implement observability across application, model and agent layers, and drive continuous improvement using telemetry and user feedback.
- Lead the transition from proof of concept to hardened, scalable production deployment.
- Embed privacy, data governance, access control, content safety, model security, auditability and Responsible AI requirements into solution design.
- Define controls for prompt injection, sensitive-data exposure, unsafe outputs, model misuse and third-party model risk.
- Partner with security, risk, legal and compliance stakeholders to align AI solutions with enterprise policies and applicable regulatory obligations.
- Drive cost transparency and FinOps practices for model usage, infrastructure and multi-cloud AI workloads.
- Lead discovery workshops, architecture assessments and design sessions with business and technology stakeholders.
- Translate business goals into AI roadmaps, solution options, implementation plans and clear value cases.
- Act as a trusted advisor to client leadership and communicate architecture choices, trade-offs, risks and outcomes in executive-ready language.
- Provide technical leadership for pursuits, proposals, solutioning and estimation, including architecture narratives and delivery models.
- Lead and mentor cross-functional teams of architects, Java engineers, AI engineers, data scientists and platform specialists.
- Create reusable accelerators, reference implementations, knowledge assets and engineering standards for the EAT competency.
- Education: BE/BTech/MCA or equivalent degree in computer science, engineering or a related discipline.
- Overall experience: 15+ years in software architecture, engineering and enterprise solution delivery.
- Java/J2EE depth: 8+ years designing and delivering enterprise-grade Java/J2EE applications, with strong Spring ecosystem experience.
- Architecture depth: 5+ years in microservices, distributed systems, API-led and event-driven architecture.
- AI delivery: 2+ years of hands-on architecture and implementation experience in AI/ML/GenAI initiatives, with evidence of production or enterprise-scale delivery.
- Leadership: Experience leading multidisciplinary engineering teams and owning architecture and delivery outcomes.
- Consulting: Strong client engagement, workshop facilitation, presales, storytelling and senior stakeholder communication skills.
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Section · 02
Skills
Api ManagementAzure DevopsBedrockcamundaAzure Aiapisai servicesbusiness continuityautomated testingaws bedrockai frameworksamazon web servicesapache airflowCapability BuildingBpmAzure OpenAIArchitectureAi SolutionsMicrosoft AzureArtificial Intelligence
Section · Company
About Ernst & Young
Ernst Young
Management Consulting
44k+
employees
2002
24 years old
Gurgaon / Gurugram, Haryana
India
₹15.3L PA avg
Avg at Ernst & Young
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