Infosys—Enterprise Architect - Gen AI

Job description

Description: 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\u2019s custom instructions, functions, and APIs. • Integrate knowledge bases, vector stores (e.g., FAISS, Pinecone, Weaviate), and APIs into a cohesive Agentic RAG architecture. Responsibilities: 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\u2019s custom instructions, functions, and APIs. • Integrate knowledge bases, vector stores (e.g., FAISS, Pinecone, Weaviate), and APIs into a cohesive Agentic RAG architecture. Skills: • 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.