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: 8+ years of experience in IT, with 5+ years focused on AI/ML engineering or AI system architecture. Proven track record in designing and delivering end-to-end AI/GenAI solutions in production environments. Strong proficiency with Azure Cloud Platform , including services like: Azure OpenAI, Azure AI Search, Azure App Services, Azure Functions, Azure Key Vault Azure Cognitive Services (Speech, Vision, Document Intelligence) Azure Kubernetes Service (AKS), Azure Container Apps Deep understanding of cloud networking, security, and identity/access management on Azure. Hands-on experience with DevOps tools (Azure DevOps, GitHub Actions, Terraform, Bicep). Expertise in backend development using Python (FastAPI, Flask) and frontend development using ReactJS . Familiarity with LLMs, Transformer architectures , vector search, LangChain, and RAG pipelines. Experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Qdrant) and semantic search techniques. Strong skills in data engineering , working with structured/unstructured data pipelines and ETL tools. Relevant certifications preferred (e.g., Microsoft Certified: Azure Solutions Architect Expert, Azure AI Engineer Associate). Excellent analytical, problem-solving, and communication skills. Passion for innovation and a track record of delivering enterprise-grade AI solutions. Nice To Have: Experience with Kubernetes , MLflow , or Ray for distributed training/deployment. Familiarity with SaaS AI product development or MLOps pipelines . Exposure to multi-modal GenAI (text, image, audio, video).