Enterprise Architect - Gen AI

Infosys; Bengaluru; On-site; INR 5,400,000 - 7,600,000; Full time

Job summary

Infosys is seeking a Cloud AI Infra Architect to design and manage secure, scalable multi-cloud infrastructures for large language models and Gen AI applications. You will drive automation, orchestrate AI agents, and collaborate with data scientists to optimize data pipelines and model operations.

The role demands deep expertise in IaC, cloud security, and AI infra, plus strong Python scripting. You will lead architecture for autonomous agents with external tool integrations, aiming for cost

Qualifications

• 12+ years of experience in managing cloud enterprise infrastructure.
• Expertise across multi-cloud architectures for LLMs and AI workloads.
• Proven IaC automation using Terraform, CloudFormation, ARM, etc.
• Strong security, governance, data privacy, and compliance focus.
• Experience with vector stores and RAG toolchains for Gen AI.
• Hands-on Python development and scripting for automation.
• Collaborative with Data Science and ML engineering teams.

Responsibilities

• Design and evolve secure, scalable multi-cloud infra for LLMs and Gen AI.
• Develop patterns to deploy and manage autonomous AI agents with external tools.
• Advance IaC adoption to provision and govern AI infrastructure.
• Optimize performance and cost for high compute AI workloads across clouds.
• Define security architectures and data governance for AI data and models.
• Collaborate on data pipelines for unstructured and vector data for Gen AI.
• Build robust MLOps/Gen AIOps CI/CD/CT/CE pipelines.
• Architect agentic workflows with RAG, LLM agents, and tools integration.

Tools

Terraform

CloudFormation

Google Deployment Manager

Bicep

Docker

Kubernetes

LangChain

Python

FAISS

Pinecone

Weaviate

OpenAI API

LLM deployment

RAG architectures

MLOps

Gen AI

Cloud security

Data governance

Python scripting

TensorFlow

PyTorch

AI infra automation

ARM

GCP

AWS

Azure

CI/CD

Job description

• Proven experience designing, implementing, and managing cloud solutions on major cloud platforms (e.g., AWS, Azure, GCP).
• Strong understanding of cloud computing concepts, architectures, and services (IaaS, PaaS, SaaS).
• Hands-on experience with cloud automation and infrastructure-as-code tools (e.g., Terraform, CloudFormation, ARM).
• Experience with cloud security best practices and tools.
• Deep expertise across compute, storage, networking, security, and AI/ML services on GCP/AWS/Azure
• LLM/Foundation Model Deployment: Experience with deploying, serving, and managing large language models (LLMs) and other foundation models.
• Vector Databases: Expertise in integrating and managing vector databases for Retrieval-Augmented Generation (RAG) architectures.
• Prompt Engineering Environments: Designing and implementing infrastructure to support prompt engineering workflows and experimentation.
• Agent orchestration & tool integration (e.g., LangChain).
• Infrastructure as Code (IaC): Terraform (expert), CloudFormation, Google Deployment Manager, Bicep.
• Programming/Scripting: Python (strong).
• Data Technologies: Data Lakes, object storage, streaming platforms (relevant to AI data).
• Security & Governance: Cloud security best practices, data privacy, compliance.

• Proven experience designing, implementing, and managing cloud solutions on major cloud platforms (e.g., AWS, Azure, GCP).
• Strong understanding of cloud computing concepts, architectures, and services (IaaS, PaaS, SaaS).
• Hands-on experience with cloud automation and infrastructure-as-code tools (e.g., Terraform, CloudFormation, ARM).
• Experience with cloud security best practices and tools.
• Deep expertise across compute, storage, networking, security, and AI/ML services on GCP/AWS/Azure
• LLM/Foundation Model Deployment: Experience with deploying, serving, and managing large language models (LLMs) and other foundation models.
• Vector Databases: Expertise in integrating and managing vector databases for Retrieval-Augmented Generation (RAG) architectures.
• Prompt Engineering Environments: Designing and implementing infrastructure to support prompt engineering workflows and experimentation.
• Agent orchestration & tool integration (e.g., LangChain).
• Infrastructure as Code (IaC): Terraform (expert), CloudFormation, Google Deployment Manager, Bicep.
• Containerization & Orchestration: Docker, Kubernetes (EKS, GKE, AKS).
• MLOps/Gen AIOps: CI/CD pipelines for AI models/agents, model versioning, monitoring.
• Programming/Scripting: Python (strong).
• Data Technologies: Data Lakes, object storage, streaming platforms (relevant to AI data).
• Security & Governance: Cloud security best practices, data privacy, compliance.

As a Cloud AI Infra Architect you should have with a minimum of 12+ years of experience in managing Cloud Enterprise infrastructure projects and driving automation through Gen AI, drive the adoption, optimization of our cloud infrastructure and services. You will be a key technical resource, responsible for designing, implementing, and maintaining secure, scalable, and cost-effective cloud solutions across our enterprise and drive optimization through Gen AI.

• Design, implement, and evolve highly available, scalable, and secure multi-cloud architectures specifically tailored for large language models (LLMs), foundation models, vector databases, prompt engineering environments, fine-tuning, and real-time inference for Gen AI.
• Develop infrastructure patterns and frameworks to support the deployment, orchestration, and management of autonomous AI agents, including their interaction with external tools, data sources, and reasoning engines.
• Drive the adoption and implementation of advanced IaC to automate the provisioning, configuration, and governance of all AI infrastructure.
• Proactively identify bottlenecks and implement innovative strategies for optimizing the performance, cost-efficiency, and resource utilization of high-compute AI workloads across all cloud providers.
• Define and enforce stringent security architectures, data governance policies, and compliance frameworks for sensitive AI data, models, and agent interactions (e.g., data privacy, responsible AI principles).
• Partner with Data Engineering to design and optimize data pipelines for large-scale, unstructured, and vector data required for Gen AI model training, fine-tuning, and retrieval-augmented generation
• Collaborate closely with Data Scientists and ML/Gen AI Engineers to design and implement robust MLOps/Gen AIOps pipelines for continuous integration, continuous delivery (CI/CD), continuous training (CT), and continuous evaluation (CE) of Gen AI models and agents.
• Architect and implement agentic workflows using RAG pipelines, LLM agents, and external tool integrations.
• Design modular, agentic systems that include planning, memory, tool use, and context-aware reasoning.
• Develop and optimize custom GPTs using advanced prompt engineering and OpenAI's custom instructions, functions, and APIs.
• Integrate knowledge bases, vector stores (e.g., FAISS, Pinecone, Weaviate), and APIs into a cohesive Agentic RAG architecture. Besides the professional qualifications of the candidates, we place great importance in addition to various forms personality profile.
• High analytical skills
• A high degree of initiative and flexibility
• High customer orientation
• High quality awareness
• Excellent verbal and written communication skills