Enterprise Architect – Gen AI

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.

Additional Responsibilities

Besides the professional qualifications of the candidates, we place great importance in addition to various forms personality profile. These include:

• High analytical skills
• A high degree of initiative and flexibility
• High customer orientation
• High quality awareness
• Excellent verbal and written communication skills

Technical and Professional Requirements

• 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.

Preferred Skills

• AgentOps
• MLOps
• Generative AI – Basic
• TensorFlow
• Python
• Machine Learning
• Artificial Intelligence – BASIC

Educational Requirements

Master Of Engineering,Master Of Technology,Bachelor Of Science,Bachelor of Engineering,Bachelor Of Technology,BCA,BTech