Generative Ai Architect

Overview

We are seeking a highly skilled and experienced Azure OpenAI Architect to lead the design and implementation of end-to-end Generative AI systems. This role goes beyond building AI models—it involves architecting robust, scalable, and secure AI-powered applications and services across the full technology stack. The ideal candidate will combine deep expertise in AI / ML, modern software engineering practices, and cloud architecture to deliver impactful business solutions using Azure OpenAI and related technologies.

You will play a pivotal role in guiding technical strategy, integrating LLMs, overseeing system design and development, and ensuring seamless deployment and operation of AI-powered applications.

Task and Responsibilities :

• Architect End-to-End AI Solutions : Lead the design of comprehensive AI systems from data ingestion and model orchestration to full-stack applications and secure deployment pipelines.
• Cloud Architecture & Infrastructure : Design and manage scalable Azure cloud infrastructure, including networking, resource provisioning, monitoring, and cost optimization.
• Security & Identity Management : Implement secure architectures using Azure Active Directory, Managed Identities, Role-Based Access Control (RBAC), Key Vault, and compliance with organizational and regulatory standards.
• Gen AI & LLM Integration : Design and deploy Generative AI applications using Azure OpenAI Service, Open Source LLMs (e.G., Llama2, Mistral), and frameworks like LangChain, Semantic Kernel, and vector databases.
• Software Engineering : Develop full-stack AI applications using Python (FastAPI) for backend services and ReactJS for user interfaces.
• DevOps & CI / CD : Automate infrastructure and deployment workflows using Azure DevOps, GitHub Actions, or Terraform for continuous integration, delivery, and monitoring.
• Data Engineering : Build and manage data pipelines for ingesting, processing, and enriching structured and unstructured data using services like Azure Data Factory, Azure Synapse, and Azure AI Document Intelligence.
• Search & RAG Implementation : Implement advanced search features and Retrieval-Augmented Generation (RAG) pipelines using Azure AI Search, vector stores, embeddings, summarization, and query transformation.
• Monitoring & Optimization : Continuously monitor solution performance and availability, and optimize AI workloads across the stack.
• Collaboration & Leadership : Work with data scientists, engineers, product managers, and business stakeholders to align solutions with organizational goals.
• Innovation & Best Practices : Stay ahead of AI trends and Azure capabilities, promoting best practices in architecture, coding, and system design.

Qualifications :

Nice To Have :