Tech S and T-AI -Architect-ISR-SeniorManager-GDSF02

Ernst & Young Advisory Services Sdn Bhd; Dadri; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

EY’s Tech S and T-AI team seeks an AI Architect to sit at the intersection of Infrastructure and AI, designing, building, and deploying AI/LLM-powered solutions for ITOps, automation, and resiliency.

The role requires strong coding in Python, experience with cloud platforms (Azure/AWS/GCP), and governance. You will mentor engineers and drive the AI Center of Excellence within the infra vertical.

Qualifications

• 16–18 years in Infrastructure/Cloud engineering or architecture, with the last 5–6 years focused on applied AI/ML/LLM work
• Solid understanding of LLM fundamentals: prompting, fine-tune, embeddings, RAG, context windows, tokens/cost
• Practical experience building agentic AI systems with tool-calling/function-calling
• Experience with vector databases
• Deep infra background: cloud architecture, networking, virtualization, ITSM, monitoring/observability, automation
• Experience with MLOps/LLMOps — model deployment, monitoring, versioning, cost governance
• Strong architecture and solutioning skills — can whiteboard an end-to-end system and defend design tradeoffs

Responsibilities

• Architect and deliver AI/ML/LLM-based solutions embedded into infrastructure operations (ITOps/AIOps, self-healing systems, predictive capacity, automated RCA)
• Design and build agentic AI workflows for infra automation — ticketing, monitoring, remediation, provisioning
• Evaluate, fine-tune, and integrate LLMs into enterprise infra tooling
• Build RAG pipelines, vector databases, and knowledge-grounding systems over infra documentation, runbooks, and CMDB data
• Write production-grade code (Python primarily; scripting in Bash/PowerShell)
• Integrate AI solutions with cloud platforms (Azure/AWS/GCP), ITSM tools (ServiceNow), observability stacks (Datadog, Prometheus/Grafana), and CI/CD pipelines
• Define and enforce AI governance guardrails — data privacy, model security, hallucination control, cost/token management
• Partner with infra leadership to identify high-ROI AI use cases and build the roadmap
• Mentor infra engineers on AI-adjacent skills; act as the internal AI Center of Excellence lead for the infra vertical

Skills

AI/ML/LLM experience

Infrastructure/Cloud engineering

LLM fundamentals

Agentic AI systems

Vector databases

MLOps/LLMOps

Cloud platform familiarity

System design & architecture

Python programming

Tools

LangGraph

AutoGen

CrewAI

Pinecone

Weaviate

FAISS

Azure AI Search

Ansible

Terraform

Datadog

Prometheus

Grafana

ServiceNow

Job description

Tech S and T-AI -Architect-ISR-SeniorManager-GDSF02

Location: Noida

Other locations: Anywhere in Country

Date: Sep 10, 2026

Requisition ID: 1742720

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.

Job Description: AI Architect – Infrastructure & Cloud

Title: AI Architect

Level: Senior Manager

Experience: 16-18 years

Role Summary

We are looking for an AI Architect to sit at the intersection of Infrastructure and Artificial Intelligence — someone who can design, build, and operationalize AI/LLM/Agentic solutions that solve real infrastructure problems: capacity planning, incident response, observability, automation, cost optimization, and resiliency engineering. This is not a pure research role — it's an applied engineering + architecture role for someone who can code, ship, and integrate AI into production infra workflows.

Key Responsibilities

• Architect and deliver AI/ML/LLM-based solutions embedded into infrastructure operations (ITOps/AIOps, self-healing systems, predictive capacity, automated RCA)
• Design and build agentic AI workflows (multi-step, tool-using agents) for infra automation — ticketing, monitoring, remediation, provisioning
• Evaluate, fine-tune, and integrate LLMs (open-source and commercial: OpenAI, Anthropic, Azure OpenAI, local/open models) into enterprise infra tooling
• Build RAG pipelines, vector databases, and knowledge-grounding systems over infra documentation, runbooks, and CMDB data
• Write production-grade code (Python primarily; scripting in Bash/PowerShell) — this role codes, doesn't just design PPTs
• Integrate AI solutions with cloud platforms (Azure/AWS/GCP), ITSM tools (ServiceNow), observability stacks (Datadog, Splunk, Prometheus/Grafana), and CI/CD pipelines
• Define and enforce AI governance guardrails — data privacy, model security, hallucination control, cost/token management
• Partner with infra leadership to identify high-ROI AI use cases and build the roadmap
• Mentor infra engineers on AI-adjacent skills; act as the internal AI Center of Excellence lead for the infra vertical

Must-Have Skills

• 16–18 years in Infrastructure/Cloud engineering or architecture, with the last 5–6 years focused on applied AI/ML/LLM work
• Solid understanding of LLM fundamentals: prompting, fine-tune, embeddings, RAG, context windows, tokens/cost
• Practical experience building agentic AI systems (LangGraph, AutoGen, CrewAI, or custom orchestration) with tool-calling/function-calling
• Experience with vector databases (Pinecone, Weaviate, FAISS, Azure AI Search, etc.)
• Deep infra background: cloud architecture, networking, virtualization, ITSM, monitoring/observability, automation (Ansible/Terraform)
• Experience with MLOps/LLMOps — model deployment, monitoring, versioning, cost governance
• Strong architecture and solutioning skills — can whiteboard an end-to-end system and defend design tradeoffs

Good-to-Have

• Certifications: AWS/Azure/GCP AI or Solutions Architect
• Exposure to enterprise AI governance/responsible AI frameworks
• Experience presenting to CXO/leadership on AI strategy and business cases
• Open-source contributions or published POCs in AI/agentic systems

Soft Skills

• Ability to translate ambiguous infra pain points into AI-solvable use cases
• Strong stakeholder management — bridges infra teams, data science teams, and business
• Comfortable being hands-on (code/architecture) as well as strategic (roadmap, governance)

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