Azure AI Architect

Azure AI Architect

JOB DESCRIPTION

We are looking for an Azure AI Architect to design and lead enterprise-grade Generative AI, agentic and machine learning solutions on Microsoft Azure – taking use cases from discovery and reference architecture through to secure, scalable, production deployment.

EXPERIENCE 10+ years in IT; 4+ years in AI/ML on Azure CORE PLATFORM FOCUS AREAS Microsoft Foundry, Azure OpenAI, Azure AI Search

Key Responsibilities

GenAI, RAG, AI agents, MLOps / LLMOps

Own the end-to-end architecture of AI solutions on Azure – from business use-case assessment and solution design to build, deployment and production operations.

• Design Generative AI and Retrieval-Augmented Generation (RAG) solutions using Azure OpenAI models, Microsoft Foundry (formerly Azure AI Foundry) and Azure AI Search, including chunking, embedding, hybrid search and semantic ranking strategies.

• Architect single- and multi-agent systems using Foundry Agent Service and frameworks such as Microsoft Agent Framework, Semantic Kernel, AutoGen or LangChain/LangGraph, including tool calling and Model Context Protocol (MCP) integrations.

• Select the right model for each workload – frontier, open-weight or small language models – balancing accuracy, latency, cost and data-residency requirements; guide prompt engineering and fine-tuning decisions.

• Define reference architectures, landing zones and reusable patterns for AI workloads, aligned with the Azure Well-Architected Framework and Cloud Adoption Framework.

• Design the data foundation for AI – ingestion, pipelines and governance across Azure Data Lake, Microsoft Fabric, Azure Databricks, Cosmos DB and Azure SQL.

• Build MLOps / LLMOps practices with Azure Machine Learning, Azure DevOps or GitHub Actions covering CI/CD, model registry, evaluation, monitoring, tracing and drift detection.

• Embed security and Responsible AI by design – Microsoft Entra ID, managed identities, private endpoints, Key Vault, content safety filters, guardrails, prompt-injection defences and audit logging.

• Optimise cost and performance of AI workloads – token usage, provisioned vs pay-as-you-go throughput, caching, scaling and quota planning.

• Integrate AI capabilities with enterprise applications and APIs using Azure API Management, Azure Functions, Logic Apps and AKS / Container Apps.

• Lead technical discovery with business and technology stakeholders; run proofs of concept and pilots and convert them into production roadmaps. • Guide and mentor AI engineers, data scientists and developers; conduct design and code reviews and set engineering standards.

• Produce architecture documentation – solution designs, HLD/LLD, decision records and runbooks. Required Skills & Experience

• 10+ years of overall IT experience, with at least 4 years architecting AI/ML solutions on Microsoft Azure and 2+ years delivering Generative AI solutions into production.

• Deep hands-on expertise in Azure OpenAI, Microsoft Foundry and Azure AI Search, with proven delivery of production-grade RAG systems.

• Hands-on experience building AI agents and orchestrating LLM workflows with at least one framework Microsoft Agent Framework, Semantic Kernel, AutoGen, LangChain or LangGraph.

• Strong working knowledge of Azure AI services – Document Intelligence, Language, Speech and Vision. Cerebraix Technologies Private Limited · Capability Cloud Page 1JOB DESCRIPTION • Solid Azure platform foundation – networking, identity (Entra ID), security, compute (AKS, Functions, App Service) and infrastructure-as-code (Bicep or Terraform).

• Experience with Azure Machine Learning and MLOps / LLMOps tooling, including model evaluation and observability. • Strong programming skills in Python; working knowledge of C# / .NET or TypeScript is an advantage.

Good understanding of vector databases, embeddings, prompt engineering and LLM evaluation techniques.

• Working knowledge of Responsible AI principles and data privacy requirements, including India's DPDP Act.

• Excellent communication skills, with the ability to present architecture choices and trade-offs to both technical and business audiences. Good to Have

• Microsoft certifications: Azure Solutions Architect Expert (AZ-305) and Azure AI Apps and Agents Developer Associate (AI-103), or the earlier Azure AI Engineer Associate (AI-102).

Experience

Microsoft Fabric, Azure Databricks or Copilot Studio.

• Exposure to multi-cloud AI platforms (AWS Bedrock, Google Vertex AI) or open-weight model hosting.

• Domain experience in BFSI, healthcare, retail or manufacturing AI use cases.