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).