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 OpenAIu2019s 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.

Enterprise Architect - Gen AI