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

EY; Ernakulam; On-site; INR 3,500,000 - 7,500,000; Full time

Job summary

EY invites an AI Architect to sit at the intersection of Infrastructure and AI, building production AI/LLM solutions for ITOps, observability, and automation. You will design, implement, and operate AI-enabled infra workflows with governance and cost controls, partnering with leadership to shape the AI roadmap.

This role emphasizes hands-on coding (Python) and end-to-end delivery, including MLOps/LLMOps practices, across Azure/AWS/GCP and ITSM tools.

Qualifications

• 16–18 years in Infrastructure/Cloud engineering or architecture, with the last 5–6 years focused on applied AI/ML/LLM work.
• Strong hands-on coding ability — Python (must), API integration, SDK usage (OpenAI/Anthropic/LangChain/LlamaIndex).
• Solid understanding of LLM fundamentals: prompting, fine-tuning, embeddings, RAG, context windows, tokens/cost.

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.
• Own POC -> pilot -> production lifecycle for AI initiatives, including MLOps/LLMOps practices.

Skills

Python

API integration

LangChain

LlamaIndex

OpenAI/Anthropic

LLM tooling

Infra automation

MLOps/LMOps

Cloud integration

System architecture

Tools

Pinecone

FAISS

Weaviate

Azure AI Search

Datadog

Grafana

Prometheus

Terraform

Ansible

ServiceNow

LangGraph

AutoGen

CrewAI

LangChain

LlamaIndex

Job description

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
• Own POC -> pilot -> production lifecycle for AI initiatives, including MLOps/LLMOps practices

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
• Strong hands-on coding ability — Python (must), API integration, SDK usage (OpenAI/Anthropic/LangChain/LlamaIndex)
• Solid understanding of LLM fundamentals: prompting, fine-tuning, 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
• Prior experience in a Big 4 / GDS / large enterprise infra environment
• 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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